field_construction/scene/gaussian_model.py

#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact  [email protected]
#

import os

import numpy as np
import torch
from plyfile import PlyData, PlyElement
from pytorch3d.transforms import quaternion_to_matrix
from simple_knn._C import distCUDA2
from torch import nn

from field_construction.scene.per_point_adam import PerPointAdam
from field_construction.utils.general_utils import (build_rotation,
                                                    build_scaling,
                                                    build_scaling_rotation,
                                                    get_expon_lr_func,
                                                    inverse_sigmoid,
                                                    strip_symmetric)
from field_construction.utils.graphics_utils import BasicPointCloud
from field_construction.utils.pose_utils import get_tensor_from_camera
from field_construction.utils.sh_utils import RGB2SH
from field_construction.utils.system_utils import mkdir_p


def dilate(bin_img, ksize=5):
    pad = (ksize - 1) // 2
    bin_img = torch.nn.functional.pad(bin_img, pad=[pad, pad, pad, pad], mode='reflect')
    out = torch.nn.functional.max_pool2d(bin_img, kernel_size=ksize, stride=1, padding=0)
    return out

def erode(bin_img, ksize=5):
    out = 1 - dilate(1 - bin_img, ksize)
    return out

class GaussianModel:

    def setup_functions(self):
        def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
            L = build_scaling_rotation(scaling_modifier * scaling, rotation)
            actual_covariance = L @ L.transpose(1, 2)
            symm = strip_symmetric(actual_covariance)
            return symm
        
        self.scaling_activation = torch.exp
        self.scaling_inverse_activation = torch.log

        self.covariance_activation = build_covariance_from_scaling_rotation

        self.opacity_activation = torch.sigmoid
        self.inverse_opacity_activation = inverse_sigmoid

        self.rotation_activation = torch.nn.functional.normalize

    def __init__(self, sh_degree : int):
        self.active_sh_degree = 0
        self.max_sh_degree = sh_degree  
        self._xyz = torch.empty(0)
        self._knn_f = torch.empty(0)
        self._features_dc = torch.empty(0)
        self._features_rest = torch.empty(0)
        self._scaling = torch.empty(0)
        self._rotation = torch.empty(0)
        self._opacity = torch.empty(0)
        self._language_feature = torch.empty(0)
        self._instance_feature=torch.empty(0)
        self.max_radii2D = torch.empty(0)
        self.max_weight = torch.empty(0)
        self.xyz_gradient_accum = torch.empty(0)
        self.xyz_gradient_accum_abs = torch.empty(0)
        self.denom = torch.empty(0)
        self.denom_abs = torch.empty(0)
        self.optimizer = None
        self.cam_optimizer = None
        self.percent_dense = 0
        self.spatial_lr_scale = 0
        self.knn_dists = None
        self.knn_idx = None
        self.setup_functions()
        self.use_app = False

    def capture(self, include_feature=False):
        if include_feature:
            return (
                self.active_sh_degree,
                self._xyz,
                self._knn_f,
                self._features_dc,
                self._features_rest,
                self._scaling,
                self._rotation,
                self._opacity,
                self._language_feature,
                self._instance_feature,
                self.max_radii2D,
                self.max_weight,
                self.xyz_gradient_accum,
                self.xyz_gradient_accum_abs,
                self.denom,
                self.denom_abs,
                self.optimizer.state_dict(),
                self.cam_optimizer.state_dict(),
                self.spatial_lr_scale,
                self.P
            )
        else:
            return (
                self.active_sh_degree,
                self._xyz,
                self._knn_f,
                self._features_dc,
                self._features_rest,
                self._scaling,
                self._rotation,
                self._opacity,
                self.max_radii2D,
                self.max_weight,
                self.xyz_gradient_accum,
                self.xyz_gradient_accum_abs,
                self.denom,
                self.denom_abs,
                self.optimizer.state_dict(),
                self.cam_optimizer.state_dict(),
                self.spatial_lr_scale,
                self.P
            )
    
    def restore(self, model_args, training_args, mode='train'):
        # Ckpt with training feature (20 arguments)
        if len(model_args) == 20:
            (self.active_sh_degree, 
            self._xyz, 
            self._knn_f,
            self._features_dc, 
            self._features_rest,
            self._scaling, 
            self._rotation, 
            self._opacity,
            self._language_feature, # Added training feature: language feature
            self._instance_feature, # Added training feature: instance feature
            self.max_radii2D, 
            self.max_weight,
            xyz_gradient_accum, 
            xyz_gradient_accum_abs,
            denom,
            denom_abs,
            opt_dict, 
            cam_opt_dict,
            self.spatial_lr_scale,
            self.P
            ) = model_args
        # Ckpt without training feature (18 arguments)
        elif len(model_args) == 18:
            (self.active_sh_degree, 
            self._xyz, 
            self._knn_f,
            self._features_dc, 
            self._features_rest,
            self._scaling, 
            self._rotation, 
            self._opacity,
            self.max_radii2D, 
            self.max_weight,
            xyz_gradient_accum, 
            xyz_gradient_accum_abs,
            denom,
            denom_abs,
            opt_dict, 
            cam_opt_dict,
            self.spatial_lr_scale,
            self.P
            ) = model_args
        if mode == 'train':
            if isinstance(self.optimizer, PerPointAdam):
                self.training_setup_pp(training_args)
            else:
                self.training_setup(training_args)
            self.xyz_gradient_accum = xyz_gradient_accum
            self.xyz_gradient_accum_abs = xyz_gradient_accum_abs
            self.denom = denom
            self.denom_abs = denom_abs
            self.optimizer.load_state_dict(opt_dict)
            self.cam_optimizer.load_state_dict(cam_opt_dict)

    @property
    def get_scaling(self):
        return self.scaling_activation(self._scaling)
        
    @property
    def get_rotation(self):
        return self.rotation_activation(self._rotation)
    
    @property
    def get_xyz(self):
        return self._xyz
    
    @property
    def get_features(self):
        features_dc = self._features_dc
        features_rest = self._features_rest
        return torch.cat((features_dc, features_rest), dim=1)
    
    @property
    def get_opacity(self):
        return self.opacity_activation(self._opacity)
    
    @property
    def get_language_feature(self):
        return self._language_feature
    
    @property
    def get_instance_feature(self):
        return self._instance_feature
    
    def get_smallest_axis(self, return_idx=False):
        rotation_matrices = self.get_rotation_matrix()
        smallest_axis_idx = self.get_scaling.min(dim=-1)[1][..., None, None].expand(-1, 3, -1)
        smallest_axis = rotation_matrices.gather(2, smallest_axis_idx)
        if return_idx:
            return smallest_axis.squeeze(dim=2), smallest_axis_idx[..., 0, 0]
        return smallest_axis.squeeze(dim=2)
    
    def get_normal(self, view_cam):
        normal_global = self.get_smallest_axis()
        gaussian_to_cam_global = view_cam.camera_center - self._xyz
        neg_mask = (normal_global * gaussian_to_cam_global).sum(-1) < 0.0
        normal_global[neg_mask] = -normal_global[neg_mask]
        return normal_global
    
    def init_RT_seq(self, cam_list):
        poses =[]
        index_mapping = {}
        for cam_idx, cam in enumerate(cam_list[1.0]):
            p = get_tensor_from_camera(cam.world_view_transform.transpose(0, 1)) # R T -> quat t
            poses.append(p)
            index_mapping[cam.uid] = cam_idx
        poses = torch.stack(poses)
        self.index_mapping = index_mapping
        self.P = poses.cuda().requires_grad_(True)

    def get_RT(self, idx):
        pose = self.P[idx]
        return pose
    
    def get_RT_test(self, idx):
        pose = self.test_P[idx]
        return pose
    
    def get_rotation_matrix(self):
        return quaternion_to_matrix(self.get_rotation)

    def get_covariance(self, scaling_modifier = 1):
        return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation)

    def oneupSHdegree(self):
        if self.active_sh_degree < self.max_sh_degree:
            self.active_sh_degree += 1

    def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):
        self.spatial_lr_scale = spatial_lr_scale
        fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda()
        fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda())
        features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda()
        features[:, :3, 0 ] = fused_color
        features[:, 3:, 1:] = 0.0

        print("Number of points at initialisation : ", fused_point_cloud.shape[0])

        dist = torch.sqrt(torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001))
        # print(f"new scale {torch.quantile(dist, 0.1)}")
        scales = torch.log(dist)[...,None].repeat(1, 3)
        rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda")
        rots[:, 0] = 1

        opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda"))

        knn_f = torch.randn((fused_point_cloud.shape[0], 6)).float().cuda()
        self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True))
        self._knn_f = nn.Parameter(knn_f.requires_grad_(True))
        self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True))
        self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True))
        self._scaling = nn.Parameter(scales.requires_grad_(True))
        self._rotation = nn.Parameter(rots.requires_grad_(True))
        self._opacity = nn.Parameter(opacities.requires_grad_(True))
        self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
        self.max_weight = torch.zeros((self.get_xyz.shape[0]), device="cuda")

        language_feature = torch.zeros((fused_point_cloud.shape[0], 3), device="cuda")
        self._language_feature = nn.Parameter(language_feature.requires_grad_(True)).requires_grad_(True)  # dont train feature at first

        # NOTE for instance distinguish
        instance_feature = torch.zeros((fused_point_cloud.shape[0], 3), device="cuda")
        self._instance_feature = nn.Parameter(instance_feature.requires_grad_(False)).requires_grad_(False)  # just train feature at last

    def training_setup(self, training_args, device):
        self.percent_dense = training_args.percent_dense
        self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device=device)
        self.xyz_gradient_accum_abs = torch.zeros((self.get_xyz.shape[0], 1), device=device)
        self.denom = torch.zeros((self.get_xyz.shape[0], 1), device=device)
        self.denom_abs = torch.zeros((self.get_xyz.shape[0], 1), device=device)
        self.abs_split_radii2D_threshold = training_args.abs_split_radii2D_threshold
        self.max_abs_split_points = training_args.max_abs_split_points
        self.max_all_points = training_args.max_all_points

        l = [
            {'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"},
            {'params': [self._knn_f], 'lr': 0.01, "name": "knn_f"},
            {'params': [self._features_dc], 'lr': training_args.feature_lr, "name": "f_dc"},
            {'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, "name": "f_rest"},
            {'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"},
            {'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"},
            {'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"},
            {'params': [self._language_feature], 'lr': training_args.language_feature_lr, "name": "language_feature"},  # semantic
            {'params': [self._instance_feature], 'lr': training_args.language_feature_lr, "name": "instance_feature"},  # instance 
        ]

        l_cam = [{'params': [self.P],'lr': training_args.rotation_lr*0.1, "name": "pose"},]
        # l += l_cam

        self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15)
        self.cam_optimizer = torch.optim.Adam(l_cam, lr=0.0, eps=1e-15)
        
        self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale,
                                                    lr_final=training_args.position_lr_final*self.spatial_lr_scale,
                                                    lr_delay_mult=training_args.position_lr_delay_mult,
                                                    max_steps=training_args.position_lr_max_steps)
        
        self.cam_scheduler_args = get_expon_lr_func(
                                                    lr_init=training_args.rotation_lr*0.1,
                                                    lr_final=training_args.rotation_lr*0.001,
                                                    lr_delay_mult=training_args.position_lr_delay_mult,
                                                    max_steps=training_args.iterations)

        
    # per-point optimizer
    def training_setup_pp(self, training_args, confidence_lr=None, device="cuda"):
        self.percent_dense = training_args.percent_dense
        self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device=device)
        self.xyz_gradient_accum_abs = torch.zeros((self.get_xyz.shape[0], 1), device=device)
        self.denom = torch.zeros((self.get_xyz.shape[0], 1), device=device)
        self.denom_abs = torch.zeros((self.get_xyz.shape[0], 1), device=device)
        self.abs_split_radii2D_threshold = training_args.abs_split_radii2D_threshold
        self.max_abs_split_points = training_args.max_abs_split_points
        self.max_all_points = training_args.max_all_points
        self.per_point_lr = confidence_lr

        l = [
            {'params': [self._xyz], 'per_point_lr': self.per_point_lr, 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"},
            {'params': [self._knn_f], 'lr': 0.01, "name": "knn_f"},
            {'params': [self._features_dc], 'lr': training_args.feature_lr, "name": "f_dc"},
            {'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, "name": "f_rest"},
            {'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"},
            {'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"},
            {'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"},
            {'params': [self._language_feature], 'lr': training_args.language_feature_lr, "name": "language_feature"},  # semantic
            {'params': [self._instance_feature], 'lr': training_args.language_feature_lr, "name": "instance_feature"},  # instance 
        ]

        l_cam = [{'params': [self.P],'lr': training_args.rotation_lr*0.1, "name": "pose"},]
        # l += l_cam

        self.optimizer = PerPointAdam(l, lr=0, betas=(0.9, 0.999), eps=1e-15, weight_decay=0.0)
        self.cam_optimizer = torch.optim.Adam(l_cam, lr=0.0, eps=1e-15)

        self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale,
                                                    lr_final=training_args.position_lr_final*self.spatial_lr_scale,
                                                    lr_delay_mult=training_args.position_lr_delay_mult,
                                                    max_steps=training_args.position_lr_max_steps)

        self.cam_scheduler_args = get_expon_lr_func(
                                                    lr_init=training_args.rotation_lr*0.1,
                                                    lr_final=training_args.rotation_lr*0.001,
                                                    lr_delay_mult=training_args.position_lr_delay_mult,
                                                    max_steps=training_args.iterations)
    
    def clip_grad(self, norm=1.0):
        for group in self.optimizer.param_groups:
            torch.nn.utils.clip_grad_norm_(group["params"][0], norm)

    def update_learning_rate(self, iteration):
        ''' Learning rate scheduling per step '''
        for param_group in self.cam_optimizer.param_groups:
            if param_group["name"] == "pose":
                lr = self.cam_scheduler_args(iteration)
                param_group['lr'] = lr

        for param_group in self.optimizer.param_groups:
            if param_group["name"] == "xyz":
                lr = self.xyz_scheduler_args(iteration)
                param_group['lr'] = lr

    def construct_list_of_attributes(self, include_feature=False):
        l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
        # All channels except the 3 DC
        for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]):
            l.append('f_dc_{}'.format(i))
        for i in range(self._features_rest.shape[1]*self._features_rest.shape[2]):
            l.append('f_rest_{}'.format(i))
        l.append('opacity')
        for i in range(self._scaling.shape[1]):
            l.append('scale_{}'.format(i))
        for i in range(self._rotation.shape[1]):
            l.append('rot_{}'.format(i))
        if include_feature:
            for i in range(self._language_feature.shape[1]):
                l.append('language_feature_{}'.format(i))
            for i in range(self._instance_feature.shape[1]):
                l.append('instance_feature_{}'.format(i))
        return l

    def save_ply(self, path, mask=None, include_feature=False):
        mkdir_p(os.path.dirname(path))

        xyz = self._xyz.detach().cpu().numpy()
        normals = np.zeros_like(xyz)
        f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
        f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
        opacities = self._opacity.detach().cpu().numpy()
        scale = self._scaling.detach().cpu().numpy()
        rotation = self._rotation.detach().cpu().numpy()
        language_feature = self._language_feature.detach().cpu().numpy()
        instance_feature = self._instance_feature.detach().cpu().numpy()

        dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes(include_feature)]
        elements = np.empty(xyz.shape[0], dtype=dtype_full)
        if include_feature:
            attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation, language_feature, instance_feature), axis=1)
        else: 
            attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)

        elements[:] = list(map(tuple, attributes))
        el = PlyElement.describe(elements, 'vertex')
        PlyData([el]).write(path)

    def reset_opacity(self):
        opacities_new = inverse_sigmoid(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01))
        optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity")
        self._opacity = optimizable_tensors["opacity"]

    def load_ply(self, path):
        plydata = PlyData.read(path)

        xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
                        np.asarray(plydata.elements[0]["y"]),
                        np.asarray(plydata.elements[0]["z"])),  axis=1)
        opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]

        features_dc = np.zeros((xyz.shape[0], 3, 1))
        features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
        features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"])
        features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"])

        extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")]
        extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1]))
        assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3
        features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))
        for idx, attr_name in enumerate(extra_f_names):
            features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])
        # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)
        features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1))

        scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
        scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1]))
        scales = np.zeros((xyz.shape[0], len(scale_names)))
        for idx, attr_name in enumerate(scale_names):
            scales[:, idx] = np.asarray(plydata.elements[0][attr_name])

        rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")]
        rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1]))
        rots = np.zeros((xyz.shape[0], len(rot_names)))
        for idx, attr_name in enumerate(rot_names):
            rots[:, idx] = np.asarray(plydata.elements[0][attr_name])

        language_feature_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("language_feature")]
        language_feature_names = sorted(language_feature_names, key = lambda x: int(x.split('_')[-1]))
        language_feature = np.zeros((xyz.shape[0], len(language_feature_names)))
        for idx, attr_name in enumerate(language_feature_names):
            language_feature[:, idx] = np.asarray(plydata.elements[0][attr_name])

        # NOTE instance
        instance_feature_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("instance_feature")]
        instance_feature_names = sorted(instance_feature_names, key = lambda x: int(x.split('_')[-1]))
        instance_feature = np.zeros((xyz.shape[0], len(instance_feature_names)))
        for idx, attr_name in enumerate(instance_feature_names):
            instance_feature[:, idx] = np.asarray(plydata.elements[0][attr_name])

        self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True))
        self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
        self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
        self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True))
        self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True))
        self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True))
        self._language_feature = nn.Parameter(torch.tensor(language_feature, dtype=torch.float, device="cuda").requires_grad_(False))
        self._instance_feature = nn.Parameter(torch.tensor(instance_feature, dtype=torch.float, device="cuda").requires_grad_(False))

        self.active_sh_degree = self.max_sh_degree

    def replace_tensor_to_optimizer(self, tensor, name):
        optimizable_tensors = {}
        for group in self.optimizer.param_groups:
            if group["name"] == name:
                stored_state = self.optimizer.state.get(group['params'][0], None)
                stored_state["exp_avg"] = torch.zeros_like(tensor)
                stored_state["exp_avg_sq"] = torch.zeros_like(tensor)

                del self.optimizer.state[group['params'][0]]
                group["params"][0] = nn.Parameter(tensor.requires_grad_(True))
                self.optimizer.state[group['params'][0]] = stored_state

                optimizable_tensors[group["name"]] = group["params"][0]
        return optimizable_tensors

    def _prune_optimizer(self, mask):
        optimizable_tensors = {}
        for group in self.optimizer.param_groups:
            stored_state = self.optimizer.state.get(group['params'][0], None)
            if stored_state is not None:
                stored_state["exp_avg"] = stored_state["exp_avg"][mask]
                stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask]

                del self.optimizer.state[group['params'][0]]
                group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True)))
                self.optimizer.state[group['params'][0]] = stored_state

                optimizable_tensors[group["name"]] = group["params"][0]
            else:
                group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True))
                optimizable_tensors[group["name"]] = group["params"][0]
        return optimizable_tensors

    def prune_points(self, mask):
        valid_points_mask = ~mask
        optimizable_tensors = self._prune_optimizer(valid_points_mask)

        self._xyz = optimizable_tensors["xyz"]
        self._knn_f = optimizable_tensors["knn_f"]
        self._features_dc = optimizable_tensors["f_dc"]
        self._features_rest = optimizable_tensors["f_rest"]
        self._opacity = optimizable_tensors["opacity"]
        self._scaling = optimizable_tensors["scaling"]
        self._rotation = optimizable_tensors["rotation"]
        self._language_feature = optimizable_tensors["language_feature"]
        self._instance_feature = optimizable_tensors["instance_feature"]

        self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask]
        self.xyz_gradient_accum_abs = self.xyz_gradient_accum_abs[valid_points_mask]

        self.denom = self.denom[valid_points_mask]
        self.denom_abs = self.denom_abs[valid_points_mask]
        self.max_radii2D = self.max_radii2D[valid_points_mask]
        self.max_weight = self.max_weight[valid_points_mask]

    def cat_tensors_to_optimizer(self, tensors_dict):
        optimizable_tensors = {}
        for group in self.optimizer.param_groups:
            assert len(group["params"]) == 1
            extension_tensor = tensors_dict[group["name"]]
            stored_state = self.optimizer.state.get(group['params'][0], None)
            if stored_state is not None:

                stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0)
                stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0)

                del self.optimizer.state[group['params'][0]]
                group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
                self.optimizer.state[group['params'][0]] = stored_state

                optimizable_tensors[group["name"]] = group["params"][0]
            else:
                group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
                optimizable_tensors[group["name"]] = group["params"][0]

        return optimizable_tensors

    def densification_postfix(self, new_xyz, new_knn_f, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_language_feature, new_instance_feature):
        d = {"xyz": new_xyz,
        "knn_f": new_knn_f,
        "f_dc": new_features_dc,
        "f_rest": new_features_rest,
        "opacity": new_opacities,
        "scaling" : new_scaling,
        "rotation" : new_rotation,
        "language_feature": new_language_feature,
        "instance_feature": new_instance_feature,
        }
        optimizable_tensors = self.cat_tensors_to_optimizer(d)
        self._xyz = optimizable_tensors["xyz"]
        self._knn_f = optimizable_tensors["knn_f"]
        self._features_dc = optimizable_tensors["f_dc"]
        self._features_rest = optimizable_tensors["f_rest"]
        self._opacity = optimizable_tensors["opacity"]
        self._scaling = optimizable_tensors["scaling"]
        self._rotation = optimizable_tensors["rotation"]
        self._language_feature = optimizable_tensors["language_feature"]
        self._instance_feature = optimizable_tensors["instance_feature"]

        self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
        self.xyz_gradient_accum_abs = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
        self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
        self.denom_abs = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
        self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
        self.max_weight = torch.zeros((self.get_xyz.shape[0]), device="cuda")

    def densify_and_split(self, grads, grad_threshold, grads_abs, grad_abs_threshold, scene_extent, max_radii2D, N=2):
        n_init_points = self.get_xyz.shape[0]
        # Extract points that satisfy the gradient condition
        padded_grad = torch.zeros((n_init_points), device="cuda")
        padded_grad[:grads.shape[0]] = grads.squeeze()
        padded_grads_abs = torch.zeros((n_init_points), device="cuda")
        padded_grads_abs[:grads_abs.shape[0]] = grads_abs.squeeze()
        padded_max_radii2D = torch.zeros((n_init_points), device="cuda")
        padded_max_radii2D[:max_radii2D.shape[0]] = max_radii2D.squeeze()

        selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False)
        selected_pts_mask = torch.logical_and(selected_pts_mask,
                                              torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent)
        if selected_pts_mask.sum() + n_init_points > self.max_all_points:
            limited_num = self.max_all_points - n_init_points
            padded_grad[~selected_pts_mask] = 0
            ratio = limited_num / float(n_init_points)
            threshold = torch.quantile(padded_grad, (1.0-ratio))
            selected_pts_mask = torch.where(padded_grad > threshold, True, False)
            # print(f"split {selected_pts_mask.sum()}, raddi2D {padded_max_radii2D.max()} ,{padded_max_radii2D.median()}")
        else:
            padded_grads_abs[selected_pts_mask] = 0
            mask = (torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent) & (padded_max_radii2D > self.abs_split_radii2D_threshold)
            padded_grads_abs[~mask] = 0
            selected_pts_mask_abs = torch.where(padded_grads_abs >= grad_abs_threshold, True, False)
            limited_num = min(self.max_all_points - n_init_points - selected_pts_mask.sum(), self.max_abs_split_points)
            if selected_pts_mask_abs.sum() > limited_num:
                ratio = limited_num / float(n_init_points)
                threshold = torch.quantile(padded_grads_abs, (1.0-ratio))
                selected_pts_mask_abs = torch.where(padded_grads_abs > threshold, True, False)
            selected_pts_mask = torch.logical_or(selected_pts_mask, selected_pts_mask_abs)
            # print(f"split {selected_pts_mask.sum()}, abs {selected_pts_mask_abs.sum()}, raddi2D {padded_max_radii2D.max()} ,{padded_max_radii2D.median()}")

        stds = self.get_scaling[selected_pts_mask].repeat(N,1)
        means =torch.zeros((stds.size(0), 3),device="cuda")
        samples = torch.normal(mean=means, std=stds)
        rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N,1,1)
        new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask].repeat(N, 1)
        new_scaling = self.scaling_inverse_activation(self.get_scaling[selected_pts_mask].repeat(N,1) / (0.8*N))
        new_rotation = self._rotation[selected_pts_mask].repeat(N,1)
        new_features_dc = self._features_dc[selected_pts_mask].repeat(N,1,1)
        new_features_rest = self._features_rest[selected_pts_mask].repeat(N,1,1)
        new_opacity = self._opacity[selected_pts_mask].repeat(N,1)
        new_knn_f = self._knn_f[selected_pts_mask].repeat(N,1)
        new_language_feature = self._language_feature[selected_pts_mask].repeat(N,1)
        new_instance_feature = self._instance_feature[selected_pts_mask].repeat(N,1)

        self.densification_postfix(new_xyz, new_knn_f, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation, new_language_feature, new_instance_feature)

        prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool)))
        self.prune_points(prune_filter)

    def densify_and_clone(self, grads, grad_threshold, scene_extent):
        n_init_points = self.get_xyz.shape[0]
        # Extract points that satisfy the gradient condition
        selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False)
        selected_pts_mask = torch.logical_and(selected_pts_mask,
                                              torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent)
        
        if selected_pts_mask.sum() + n_init_points > self.max_all_points:
            limited_num = self.max_all_points - n_init_points
            grads_tmp = grads.squeeze().clone()
            grads_tmp[~selected_pts_mask] = 0
            ratio = min(limited_num / float(n_init_points), 1)
            threshold = torch.quantile(grads_tmp, (1.0-ratio))
            selected_pts_mask = torch.where(grads_tmp > threshold, True, False)

        if selected_pts_mask.sum() > 0:
            # print(f"clone {selected_pts_mask.sum()}")
            new_xyz = self._xyz[selected_pts_mask]

            stds = self.get_scaling[selected_pts_mask]
            means =torch.zeros((stds.size(0), 3),device="cuda")
            samples = torch.normal(mean=means, std=stds)
            rots = build_rotation(self._rotation[selected_pts_mask])
            new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask]
            
            new_features_dc = self._features_dc[selected_pts_mask]
            new_features_rest = self._features_rest[selected_pts_mask]
            new_opacities = self._opacity[selected_pts_mask]
            new_scaling = self._scaling[selected_pts_mask]
            new_rotation = self._rotation[selected_pts_mask]
            new_knn_f = self._knn_f[selected_pts_mask]
            new_language_feature = self._language_feature[selected_pts_mask]
            new_instance_feature = self._instance_feature[selected_pts_mask]

            self.densification_postfix(new_xyz, new_knn_f, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_language_feature, new_instance_feature)

    def densify_and_prune(self, max_grad, abs_max_grad, min_opacity, extent, max_screen_size):
        grads = self.xyz_gradient_accum / self.denom
        grads_abs = self.xyz_gradient_accum_abs / self.denom_abs
        grads[grads.isnan()] = 0.0
        grads_abs[grads_abs.isnan()] = 0.0
        max_radii2D = self.max_radii2D.clone()

        self.densify_and_clone(grads, max_grad, extent)
        self.densify_and_split(grads, max_grad, grads_abs, abs_max_grad, extent, max_radii2D)

        prune_mask = (self.get_opacity < min_opacity).squeeze()

        if max_screen_size:
            big_points_vs = self.max_radii2D > max_screen_size
            big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent
            prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws)
        self.prune_points(prune_mask)
        # print(f"all points {self._xyz.shape[0]}")
        torch.cuda.empty_cache()

    def add_densification_stats(self, viewspace_point_tensor, viewspace_point_tensor_abs, update_filter):
        self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True)
        self.xyz_gradient_accum_abs[update_filter] += torch.norm(viewspace_point_tensor_abs.grad[update_filter,:2], dim=-1, keepdim=True)
        self.denom[update_filter] += 1
        self.denom_abs[update_filter] += 1

    def get_points_depth_in_depth_map(self, fov_camera, depth, points_in_camera_space, scale=1):
        st = max(int(scale/2)-1,0)
        depth_view = depth[None,:,st::scale,st::scale]
        W, H = int(fov_camera.image_width/scale), int(fov_camera.image_height/scale)
        depth_view = depth_view[:H, :W]
        pts_projections = torch.stack(
                        [points_in_camera_space[:,0] * fov_camera.Fx / points_in_camera_space[:,2] + fov_camera.Cx,
                         points_in_camera_space[:,1] * fov_camera.Fy / points_in_camera_space[:,2] + fov_camera.Cy], -1).float()/scale
        mask = (pts_projections[:, 0] > 0) & (pts_projections[:, 0] < W) &\
               (pts_projections[:, 1] > 0) & (pts_projections[:, 1] < H) & (points_in_camera_space[:,2] > 0.1)

        pts_projections[..., 0] /= ((W - 1) / 2)
        pts_projections[..., 1] /= ((H - 1) / 2)
        pts_projections -= 1
        pts_projections = pts_projections.view(1, -1, 1, 2)
        map_z = torch.nn.functional.grid_sample(input=depth_view,
                                                grid=pts_projections,
                                                mode='bilinear',
                                                padding_mode='border',
                                                align_corners=True
                                                )[0, :, :, 0]
        return map_z, mask
    
    def get_points_from_depth(self, fov_camera, depth, scale=1):
        st = int(max(int(scale/2)-1,0))
        depth_view = depth.squeeze()[st::scale,st::scale]
        rays_d = fov_camera.get_rays(scale=scale)
        depth_view = depth_view[:rays_d.shape[0], :rays_d.shape[1]]
        pts = (rays_d * depth_view[..., None]).reshape(-1,3)
        R = torch.tensor(fov_camera.R).float().cuda()
        T = torch.tensor(fov_camera.T).float().cuda()
        pts = (pts-T)@R.transpose(-1,-2)
        return pts
    
    def change_reqiures_grad(self, change, iteration, quiet=True):
        if change == "geometry":
            self._xyz.requires_grad_(True)
            self._knn_f.requires_grad_(True)
            self._features_dc.requires_grad_(True)
            self._features_rest.requires_grad_(True)
            self._scaling.requires_grad_(True)
            self._rotation.requires_grad_(True)
            self._opacity.requires_grad_(True)
            self.P.requires_grad_(True)
            self._language_feature.requires_grad_(False)
            self._instance_feature.requires_grad_(False)
            if not quiet:
                print(f'\n[ITER {iteration}] Training gaussian params')
        elif change == 'semantic': 
            self._xyz.requires_grad_(True)
            self._knn_f.requires_grad_(True)
            self._features_dc.requires_grad_(True)
            self._features_rest.requires_grad_(True)
            self._scaling.requires_grad_(True)
            self._rotation.requires_grad_(True)
            self._opacity.requires_grad_(True)
            self.P.requires_grad_(True)
            self._language_feature.requires_grad_(True)
            self._instance_feature.requires_grad_(False)
            if not quiet:
                print(f'\n[ITER {iteration}] Training gaussian params and language feature')
        elif change == 'semantic_only': 
            self._xyz.requires_grad_(False)
            self._knn_f.requires_grad_(False)
            self._features_dc.requires_grad_(False)
            self._features_rest.requires_grad_(False)
            self._scaling.requires_grad_(False)
            self._rotation.requires_grad_(False)
            self._opacity.requires_grad_(False)
            self.P.requires_grad_(False)
            self._language_feature.requires_grad_(True)
            self._instance_feature.requires_grad_(False)
            if not quiet:
                print(f'\n[ITER {iteration}] Training language feature')
        elif change == 'instance': 
            self._xyz.requires_grad_(False)
            self._knn_f.requires_grad_(False)
            self._features_dc.requires_grad_(False)
            self._features_rest.requires_grad_(False)
            self._scaling.requires_grad_(False)
            self._rotation.requires_grad_(False)
            self._opacity.requires_grad_(False)
            self.P.requires_grad_(False)
            self._language_feature.requires_grad_(False)
            self._instance_feature.requires_grad_(True)
            if not quiet:
                print(f'\n[ITER {iteration}] Training instance feature')
        elif change == "pose_only":
            self._xyz.requires_grad_(False)
            self._knn_f.requires_grad_(False)
            self._features_dc.requires_grad_(False)
            self._features_rest.requires_grad_(False)
            self._scaling.requires_grad_(False)
            self._rotation.requires_grad_(False)
            self._opacity.requires_grad_(False)
            self.P.requires_grad_(True)
            self._language_feature.requires_grad_(False)
            self._instance_feature.requires_grad_(False)
            if not quiet:
                print(f'\n[ITER {iteration}] Training instance feature')
        elif change == 'finetune':
            self._xyz.requires_grad_(False)
            self._knn_f.requires_grad_(False)
            self._features_dc.requires_grad_(True)
            self._features_rest.requires_grad_(True)
            self._scaling.requires_grad_(False)
            self._rotation.requires_grad_(False)
            self._opacity.requires_grad_(False)
            self.P.requires_grad_(False)
            self._language_feature.requires_grad_(False)
            self._instance_feature.requires_grad_(False)
            if not quiet:
                print(f'\n[ITER {iteration}] finetune')
        else:
            raise ValueError('Unknown type!')