code/scene/ground_model.py

import torch
import numpy as np
from utils.general_utils import inverse_sigmoid, get_expon_lr_func, build_rotation
from torch import nn
import os
from utils.system_utils import mkdir_p
from plyfile import PlyData, PlyElement
from utils.sh_utils import RGB2SH, SH2RGB
from simple_knn._C import distCUDA2
from utils.graphics_utils import BasicPointCloud
from utils.general_utils import strip_symmetric, build_scaling_rotation
import open3d as o3d
import math
from utils.graphics_utils import BasicPointCloud
from utils.sh_utils import RGB2SH
                
class GroundModel:

    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 = torch.logit

        self.rotation_activation = torch.nn.functional.normalize


    def __init__(self, sh_degree: int, ground_pcd: BasicPointCloud=None, model_args=None, finetune=False):
        assert not ((ground_pcd is None) and (model_args is None)), "Need at least one way of initialization"
        self.active_sh_degree = 0
        self.max_sh_degree = sh_degree 

        self.scale = 0.1
        
        if ground_pcd is not None:
            self._xyz = nn.Parameter(torch.from_numpy(ground_pcd.points).float().cuda())
            fused_color = RGB2SH(torch.tensor(np.asarray(ground_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
            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._feats3D = torch.zeros((self._xyz.shape[0], 20)).cuda()
            self._feats3D[:, 1] = 1
            self._feats3D = nn.Parameter(self._feats3D)
            self._rotation = torch.zeros((self._xyz.shape[0], 4)).cuda()
            self._rotation[:, 0] = 1
            self._opacity = inverse_sigmoid(torch.ones((self._xyz.shape[0], 1)).cuda() * 0.99)
            self._scaling = nn.Parameter(torch.ones((self._xyz.shape[0], 2)).float().cuda() * math.log(self.scale))

            self.max_radii2D = torch.zeros((self._xyz.shape[0]), device="cuda")
            self.percent_dense = 0.01
            self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
            self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
        else:
            self.restore(model_args)

        if finetune:
            self.param_groups = [
                {'params': [self._features_dc], 'lr': 2.5e-3, "name": "f_dc"},
                {'params': [self._features_rest], 'lr': 2.5e-3 / 20.0, "name": "f_rest"},
                {'params': [self._feats3D], 'lr': 1e-3, "name": "feats3D"},
            ]
        else:
            self.param_groups = [
                {'params': [self._xyz], 'lr': 1.6e-4, "name": "xyz"},
                {'params': [self._features_dc], 'lr': 2.5e-3, "name": "f_dc"},
                {'params': [self._features_rest], 'lr': 2.5e-3 / 20.0, "name": "f_rest"},
                {'params': [self._feats3D], 'lr': 1e-2, "name": "feats3D"},
                {'params': [self._opacity], 'lr': 0.05, "name": "opacity"},
                {'params': [self._scaling], 'lr': 1e-3, "name": "scaling"},
            ]
        self.optimizer = torch.optim.Adam(self.param_groups, lr=0.0, eps=1e-15)
        self.setup_functions()

    def capture(self):
        return (
            self.active_sh_degree,
            self._xyz,
            # self._y,
            # self._z,
            self._features_dc,
            self._features_rest,
            self._feats3D,
            self._scaling,
            self._rotation,
            self._opacity,
        )
    
    def restore(self, model_args):
        (self.active_sh_degree, 
        self._xyz,
        # self._y,
        # self._z,
        self._features_dc, 
        self._features_rest,
        self._feats3D,
        self._scaling,
        self._rotation, 
        self._opacity) = model_args

    @property
    def get_scaling(self):
        scale_y = torch.ones_like(self._xyz[:, 0]) * math.log(0.001)
        scaling = torch.stack((self._scaling[:, 0], scale_y, self._scaling[:, 1]), dim=1).cuda()
        # scaling = torch.stack((self._scaling, scale_y, self._scaling), dim=1).cuda()
        return self.scaling_activation(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_3D_features(self):
        return torch.softmax(self._feats3D, dim=-1)
    
    @property
    def get_opacity(self):
        return self.opacity_activation(self._opacity)
    
    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 construct_list_of_attributes(self):
        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))
        for i in range(self._feats3D.shape[1]):
            l.append('semantic_{}'.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))
        return l

    def save_ply(self, path):
        mkdir_p(os.path.dirname(path))

        xyz = self.get_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()
        feats3D = self._feats3D.detach().cpu().numpy()
        opacities = self._opacity.detach().cpu().numpy()
        scale = self._scaling.detach().cpu().numpy()
        rotation = self._rotation.detach().cpu().numpy()

        dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()]

        elements = np.empty(xyz.shape[0], dtype=dtype_full)
        attributes = np.concatenate((xyz, normals, f_dc, f_rest, feats3D, opacities, scale, rotation), axis=1)
        elements[:] = list(map(tuple, attributes))
        el = PlyElement.describe(elements, 'vertex')
        PlyData([el]).write(path)

    def save_vis_ply(self, path):
        mkdir_p(os.path.dirname(path))
        xyz = self.get_xyz.detach().cpu().numpy()
        pcd = o3d.geometry.PointCloud()
        pcd.points = o3d.utility.Vector3dVector(xyz)
        colors = SH2RGB(self._features_dc[:, 0, :].detach().cpu().numpy()).clip(0, 1)
        pcd.colors = o3d.utility.Vector3dVector(colors)
        o3d.io.write_point_cloud(path, pcd)

    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 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)
                if stored_state is not 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]
                else:
                    group["params"][0] = nn.Parameter(tensor.requires_grad_(True))
                    optimizable_tensors[group["name"]] = group["params"][0]
        return optimizable_tensors

    def _prune_optimizer(self, mask):
        optimizable_tensors = {}
        for group in self.optimizer.param_groups:
            if group['name'] == 'appearance_model':
                continue
            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._features_dc = optimizable_tensors["f_dc"]
        self._features_rest = optimizable_tensors["f_rest"]
        self._feats3D = optimizable_tensors["feats3D"]
        self._opacity = optimizable_tensors["opacity"]
        self._scaling = optimizable_tensors["scaling"]
        self._rotation = self._rotation[0, :].repeat((self._xyz.shape[0], 1))

        self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask]

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

    def cat_tensors_to_optimizer(self, tensors_dict):
        optimizable_tensors = {}
        for group in self.optimizer.param_groups:
            if group['name'] not in tensors_dict:
                continue
            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_features_dc, new_features_rest, new_feats3D, new_opacities, new_scaling, new_rotation):
        d = {"xyz": new_xyz,
        "f_dc": new_features_dc,
        "f_rest": new_features_rest,
        "feats3D": new_feats3D,
        "opacity": new_opacities,
        "scaling" : new_scaling}

        optimizable_tensors = self.cat_tensors_to_optimizer(d)
        self._xyz = optimizable_tensors["xyz"]
        self._features_dc = optimizable_tensors["f_dc"]
        self._feats3D = optimizable_tensors["feats3D"]
        self._features_rest = optimizable_tensors["f_rest"]
        self._opacity = optimizable_tensors["opacity"]
        self._scaling = optimizable_tensors["scaling"]
        self._rotation = self._rotation[0, :].repeat((self._xyz.shape[0], 1))

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

    def densify_and_split(self, grads, grad_threshold, scene_extent, 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()
        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)

        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))[:, [0,2]]
        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_feats3D = self._feats3D[selected_pts_mask].repeat(N,1)
        new_opacity = self._opacity[selected_pts_mask].repeat(N,1)

        self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_feats3D, new_opacity, new_scaling, new_rotation)

        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):
        # 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)
        
        new_xyz = self._xyz[selected_pts_mask]
        new_features_dc = self._features_dc[selected_pts_mask]
        new_features_rest = self._features_rest[selected_pts_mask]
        new_feats3D = self._feats3D[selected_pts_mask]
        new_opacities = self._opacity[selected_pts_mask]
        new_scaling = self._scaling[selected_pts_mask]
        new_rotation = self._rotation[selected_pts_mask]

        self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_feats3D, new_opacities, new_scaling, new_rotation)

    def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size):
        grads = self.xyz_gradient_accum / self.denom
        grads[grads.isnan()] = 0.0

        self.densify_and_clone(grads, max_grad, extent)
        self.densify_and_split(grads, max_grad, extent)

        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 > 1.0
            prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws)
        self.prune_points(prune_mask)

        torch.cuda.empty_cache()

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