674 lines
34 KiB
Python
674 lines
34 KiB
Python
#
|
|
# 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 george.drettakis@inria.fr
|
|
#
|
|
|
|
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
|
|
import open3d as o3d
|
|
from utils.system_utils import mkdir_p
|
|
from plyfile import PlyData, PlyElement
|
|
from random import randint
|
|
from utils.sh_utils import RGB2SH
|
|
from simple_knn._C import distCUDA2
|
|
from utils.graphics_utils import BasicPointCloud
|
|
from utils.general_utils import strip_symmetric, build_scaling_rotation
|
|
# from utils.point_utils import addpoint, combine_pointcloud, downsample_point_cloud_open3d, find_indices_in_A
|
|
from scene.deformation import deform_network
|
|
from scene.regulation import compute_plane_smoothness
|
|
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, args):
|
|
self.active_sh_degree = 0
|
|
self.max_sh_degree = sh_degree
|
|
self._xyz = torch.empty(0)
|
|
# self._deformation = torch.empty(0)
|
|
self._deformation = deform_network(args)
|
|
# self.grid = TriPlaneGrid()
|
|
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.max_radii2D = torch.empty(0)
|
|
self.xyz_gradient_accum = torch.empty(0)
|
|
self.denom = torch.empty(0)
|
|
self.optimizer = None
|
|
self.percent_dense = 0
|
|
self.spatial_lr_scale = 0
|
|
self._deformation_table = torch.empty(0)
|
|
self.setup_functions()
|
|
|
|
def capture(self):
|
|
return (
|
|
self.active_sh_degree,
|
|
self._xyz,
|
|
self._deformation.state_dict(),
|
|
self._deformation_table,
|
|
# self.grid,
|
|
self._features_dc,
|
|
self._features_rest,
|
|
self._scaling,
|
|
self._rotation,
|
|
self._opacity,
|
|
self.max_radii2D,
|
|
self.xyz_gradient_accum,
|
|
self.denom,
|
|
self.optimizer.state_dict(),
|
|
self.spatial_lr_scale,
|
|
)
|
|
|
|
def restore(self, model_args, training_args):
|
|
(self.active_sh_degree,
|
|
self._xyz,
|
|
deform_state,
|
|
self._deformation_table,
|
|
|
|
# self.grid,
|
|
self._features_dc,
|
|
self._features_rest,
|
|
self._scaling,
|
|
self._rotation,
|
|
self._opacity,
|
|
self.max_radii2D,
|
|
xyz_gradient_accum,
|
|
denom,
|
|
opt_dict,
|
|
self.spatial_lr_scale) = model_args
|
|
self._deformation.load_state_dict(deform_state)
|
|
self.training_setup(training_args)
|
|
self.xyz_gradient_accum = xyz_gradient_accum
|
|
self.denom = denom
|
|
self.optimizer.load_state_dict(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)
|
|
|
|
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, time_line: int):
|
|
self.spatial_lr_scale = spatial_lr_scale
|
|
# breakpoint()
|
|
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])
|
|
|
|
dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001)
|
|
scales = torch.log(torch.sqrt(dist2))[...,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"))
|
|
|
|
self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True))
|
|
self._deformation = self._deformation.to("cuda")
|
|
# self.grid = self.grid.to("cuda")
|
|
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._deformation_table = torch.gt(torch.ones((self.get_xyz.shape[0]),device="cuda"),0)
|
|
def training_setup(self, training_args):
|
|
self.percent_dense = training_args.percent_dense
|
|
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._deformation_accum = torch.zeros((self.get_xyz.shape[0],3),device="cuda")
|
|
|
|
|
|
l = [
|
|
{'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"},
|
|
{'params': list(self._deformation.get_mlp_parameters()), 'lr': training_args.deformation_lr_init * self.spatial_lr_scale, "name": "deformation"},
|
|
{'params': list(self._deformation.get_grid_parameters()), 'lr': training_args.grid_lr_init * self.spatial_lr_scale, "name": "grid"},
|
|
{'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"}
|
|
|
|
]
|
|
|
|
self.optimizer = torch.optim.Adam(l, 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.deformation_scheduler_args = get_expon_lr_func(lr_init=training_args.deformation_lr_init*self.spatial_lr_scale,
|
|
lr_final=training_args.deformation_lr_final*self.spatial_lr_scale,
|
|
lr_delay_mult=training_args.deformation_lr_delay_mult,
|
|
max_steps=training_args.position_lr_max_steps)
|
|
self.grid_scheduler_args = get_expon_lr_func(lr_init=training_args.grid_lr_init*self.spatial_lr_scale,
|
|
lr_final=training_args.grid_lr_final*self.spatial_lr_scale,
|
|
lr_delay_mult=training_args.deformation_lr_delay_mult,
|
|
max_steps=training_args.position_lr_max_steps)
|
|
|
|
def update_learning_rate(self, iteration):
|
|
''' Learning rate scheduling per step '''
|
|
for param_group in self.optimizer.param_groups:
|
|
if param_group["name"] == "xyz":
|
|
lr = self.xyz_scheduler_args(iteration)
|
|
param_group['lr'] = lr
|
|
# return lr
|
|
if "grid" in param_group["name"]:
|
|
lr = self.grid_scheduler_args(iteration)
|
|
param_group['lr'] = lr
|
|
# return lr
|
|
elif param_group["name"] == "deformation":
|
|
lr = self.deformation_scheduler_args(iteration)
|
|
param_group['lr'] = lr
|
|
# return lr
|
|
|
|
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))
|
|
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 compute_deformation(self,time):
|
|
|
|
deform = self._deformation[:,:,:time].sum(dim=-1)
|
|
xyz = self._xyz + deform
|
|
return xyz
|
|
# def save_ply_dynamic(path):
|
|
# for time in range(self._deformation.shape(-1)):
|
|
# xyz = self.compute_deformation(time)
|
|
def load_model(self, path):
|
|
print("loading model from exists{}".format(path))
|
|
weight_dict = torch.load(os.path.join(path,"deformation.pth"),map_location="cuda")
|
|
self._deformation.load_state_dict(weight_dict)
|
|
self._deformation = self._deformation.to("cuda")
|
|
self._deformation_table = torch.gt(torch.ones((self.get_xyz.shape[0]),device="cuda"),0)
|
|
self._deformation_accum = torch.zeros((self.get_xyz.shape[0],3),device="cuda")
|
|
if os.path.exists(os.path.join(path, "deformation_table.pth")):
|
|
self._deformation_table = torch.load(os.path.join(path, "deformation_table.pth"),map_location="cuda")
|
|
if os.path.exists(os.path.join(path, "deformation_accum.pth")):
|
|
self._deformation_accum = torch.load(os.path.join(path, "deformation_accum.pth"),map_location="cuda")
|
|
self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
|
|
# print(self._deformation.deformation_net.grid.)
|
|
def save_deformation(self, path):
|
|
torch.save(self._deformation.state_dict(),os.path.join(path, "deformation.pth"))
|
|
torch.save(self._deformation_table,os.path.join(path, "deformation_table.pth"))
|
|
torch.save(self._deformation_accum,os.path.join(path, "deformation_accum.pth"))
|
|
def save_ply(self, path):
|
|
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()
|
|
|
|
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, 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])
|
|
|
|
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.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:
|
|
if len(group["params"]) > 1:
|
|
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._opacity = optimizable_tensors["opacity"]
|
|
self._scaling = optimizable_tensors["scaling"]
|
|
self._rotation = optimizable_tensors["rotation"]
|
|
self._deformation_accum = self._deformation_accum[valid_points_mask]
|
|
self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask]
|
|
self._deformation_table = self._deformation_table[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 len(group["params"])>1: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_opacities, new_scaling, new_rotation, new_deformation_table):
|
|
d = {"xyz": new_xyz,
|
|
"f_dc": new_features_dc,
|
|
"f_rest": new_features_rest,
|
|
"opacity": new_opacities,
|
|
"scaling" : new_scaling,
|
|
"rotation" : new_rotation,
|
|
# "deformation": new_deformation
|
|
}
|
|
|
|
optimizable_tensors = self.cat_tensors_to_optimizer(d)
|
|
self._xyz = optimizable_tensors["xyz"]
|
|
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._deformation = optimizable_tensors["deformation"]
|
|
|
|
self._deformation_table = torch.cat([self._deformation_table,new_deformation_table],-1)
|
|
self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
|
|
self._deformation_accum = torch.zeros((self.get_xyz.shape[0], 3), 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)
|
|
|
|
# breakpoint()
|
|
selected_pts_mask = torch.logical_and(selected_pts_mask,
|
|
torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent)
|
|
if not selected_pts_mask.any():
|
|
return
|
|
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_deformation_table = self._deformation_table[selected_pts_mask].repeat(N)
|
|
self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation, new_deformation_table)
|
|
|
|
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, density_threshold=20, displacement_scale=20, model_path=None, iteration=None, stage=None):
|
|
grads_accum_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False)
|
|
|
|
# 主动增加稀疏点云
|
|
# if not hasattr(self,"voxel_size"):
|
|
# self.voxel_size = 8
|
|
# if not hasattr(self,"density_threshold"):
|
|
# self.density_threshold = density_threshold
|
|
# if not hasattr(self,"displacement_scale"):
|
|
# self.displacement_scale = displacement_scale
|
|
# point_cloud = self.get_xyz.detach().cpu()
|
|
# sparse_point_mask = self.downsample_point(point_cloud)
|
|
# _, low_density_points, new_points, low_density_index = addpoint(point_cloud[sparse_point_mask],density_threshold=self.density_threshold,displacement_scale=self.displacement_scale,iter_pass=0)
|
|
# sparse_point_mask = sparse_point_mask.to(grads_accum_mask)
|
|
# low_density_index = low_density_index.to(grads_accum_mask)
|
|
# if new_points.shape[0] < 100 :
|
|
# self.density_threshold /= 2
|
|
# self.displacement_scale /= 2
|
|
# print("reduce diplacement_scale to: ",self.displacement_scale)
|
|
# global_mask = torch.zeros((point_cloud.shape[0]), dtype=torch.bool).to(grads_accum_mask)
|
|
# global_mask[sparse_point_mask] = low_density_index
|
|
# selected_pts_mask_grow = torch.logical_and(global_mask, grads_accum_mask)
|
|
# print("降采样点云:",sparse_point_mask.sum(),"选中的稀疏点云：",global_mask.sum(),"梯度累计点云：",grads_accum_mask.sum(),"选中增长点云：",selected_pts_mask_grow.sum())
|
|
# Extract points that satisfy the gradient condition
|
|
selected_pts_mask = torch.logical_and(grads_accum_mask,
|
|
torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent)
|
|
# breakpoint()
|
|
new_xyz = self._xyz[selected_pts_mask]
|
|
# - 0.001 * self._xyz.grad[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_deformation_table = self._deformation_table[selected_pts_mask]
|
|
# if opt.add_point:
|
|
# selected_xyz, grow_xyz = self.add_point_by_mask(selected_pts_mask_grow.to(self.get_xyz.device), self.displacement_scale)
|
|
self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_deformation_table)
|
|
# print("被动增加点云：",selected_xyz.shape[0])
|
|
# print("主动增加点云：",selected_pts_mask.sum())
|
|
# if model_path is not None and iteration is not None:
|
|
# point = combine_pointcloud(self.get_xyz.detach().cpu().numpy(), new_xyz.detach().cpu().numpy(), selected_xyz.detach().cpu().numpy())
|
|
# write_path = os.path.join(model_path,"add_point_cloud")
|
|
# os.makedirs(write_path,exist_ok=True)
|
|
# o3d.io.write_point_cloud(os.path.join(write_path,f"iteration_{stage}{iteration}.ply"),point)
|
|
# print("write output.")
|
|
@property
|
|
def get_aabb(self):
|
|
return self._deformation.get_aabb
|
|
def get_displayment(self,selected_point, point, perturb):
|
|
xyz_max, xyz_min = self.get_aabb
|
|
displacements = torch.randn(selected_point.shape[0], 3).to(selected_point) * perturb
|
|
final_point = selected_point + displacements
|
|
|
|
mask_a = final_point<xyz_max
|
|
mask_b = final_point>xyz_min
|
|
mask_c = mask_a & mask_b
|
|
mask_d = mask_c.all(dim=1)
|
|
final_point = final_point[mask_d]
|
|
|
|
# while (mask_d.sum()/final_point.shape[0])<0.5:
|
|
# perturb/=2
|
|
# displacements = torch.randn(selected_point.shape[0], 3).to(selected_point) * perturb
|
|
# final_point = selected_point + displacements
|
|
# mask_a = final_point<xyz_max
|
|
# mask_b = final_point>xyz_min
|
|
# mask_c = mask_a & mask_b
|
|
# mask_d = mask_c.all(dim=1)
|
|
# final_point = final_point[mask_d]
|
|
return final_point, mask_d
|
|
def add_point_by_mask(self, selected_pts_mask, perturb=0):
|
|
selected_xyz = self._xyz[selected_pts_mask]
|
|
new_xyz, mask = self.get_displayment(selected_xyz, self.get_xyz.detach(),perturb)
|
|
# displacements = torch.randn(selected_xyz.shape[0], 3).to(self._xyz) * perturb
|
|
|
|
# new_xyz = selected_xyz + displacements
|
|
# - 0.001 * self._xyz.grad[selected_pts_mask]
|
|
new_features_dc = self._features_dc[selected_pts_mask][mask]
|
|
new_features_rest = self._features_rest[selected_pts_mask][mask]
|
|
new_opacities = self._opacity[selected_pts_mask][mask]
|
|
|
|
new_scaling = self._scaling[selected_pts_mask][mask]
|
|
new_rotation = self._rotation[selected_pts_mask][mask]
|
|
new_deformation_table = self._deformation_table[selected_pts_mask][mask]
|
|
|
|
self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_deformation_table)
|
|
return selected_xyz, new_xyz
|
|
def downsample_point(self, point_cloud):
|
|
if not hasattr(self,"voxel_size"):
|
|
self.voxel_size = 8
|
|
point_downsample = point_cloud
|
|
flag = False
|
|
while point_downsample.shape[0]>1000:
|
|
if flag:
|
|
self.voxel_size+=8
|
|
point_downsample = downsample_point_cloud_open3d(point_cloud,voxel_size=self.voxel_size)
|
|
flag = True
|
|
print("point size:",point_downsample.shape[0])
|
|
# downsampled_point_mask = torch.eq(point_downsample.view(1,-1,3), point_cloud.view(-1,1,3)).all(dim=1)
|
|
downsampled_point_index = find_indices_in_A(point_cloud, point_downsample)
|
|
downsampled_point_mask = torch.zeros((point_cloud.shape[0]), dtype=torch.bool).to(point_downsample.device)
|
|
downsampled_point_mask[downsampled_point_index]=True
|
|
return downsampled_point_mask
|
|
def grow(self, density_threshold=20, displacement_scale=20, model_path=None, iteration=None, stage=None):
|
|
if not hasattr(self,"voxel_size"):
|
|
self.voxel_size = 8
|
|
if not hasattr(self,"density_threshold"):
|
|
self.density_threshold = density_threshold
|
|
if not hasattr(self,"displacement_scale"):
|
|
self.displacement_scale = displacement_scale
|
|
flag = False
|
|
point_cloud = self.get_xyz.detach().cpu()
|
|
point_downsample = point_cloud.detach()
|
|
downsampled_point_index = self.downsample_point(point_downsample)
|
|
|
|
|
|
_, low_density_points, new_points, low_density_index = addpoint(point_cloud[downsampled_point_index],density_threshold=self.density_threshold,displacement_scale=self.displacement_scale,iter_pass=0)
|
|
if new_points.shape[0] < 100 :
|
|
self.density_threshold /= 2
|
|
self.displacement_scale /= 2
|
|
print("reduce diplacement_scale to: ",self.displacement_scale)
|
|
|
|
elif new_points.shape[0] == 0:
|
|
print("no point added")
|
|
return
|
|
global_mask = torch.zeros((point_cloud.shape[0]), dtype=torch.bool)
|
|
|
|
global_mask[downsampled_point_index] = low_density_index
|
|
global_mask
|
|
selected_xyz, new_xyz = self.add_point_by_mask(global_mask.to(self.get_xyz.device), self.displacement_scale)
|
|
print("point growing,add point num:",global_mask.sum())
|
|
if model_path is not None and iteration is not None:
|
|
point = combine_pointcloud(point_cloud, selected_xyz.detach().cpu().numpy(), new_xyz.detach().cpu().numpy())
|
|
write_path = os.path.join(model_path,"add_point_cloud")
|
|
os.makedirs(write_path,exist_ok=True)
|
|
o3d.io.write_point_cloud(os.path.join(write_path,f"iteration_{stage}{iteration}.ply"),point)
|
|
return
|
|
def prune(self, max_grad, min_opacity, extent, max_screen_size):
|
|
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(prune_mask, big_points_vs)
|
|
|
|
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 densify(self, max_grad, min_opacity, extent, max_screen_size, density_threshold, displacement_scale, model_path=None, iteration=None, stage=None):
|
|
grads = self.xyz_gradient_accum / self.denom
|
|
grads[grads.isnan()] = 0.0
|
|
|
|
self.densify_and_clone(grads, max_grad, extent, density_threshold, displacement_scale, model_path, iteration, stage)
|
|
self.densify_and_split(grads, max_grad, extent)
|
|
def standard_constaint(self):
|
|
|
|
means3D = self._xyz.detach()
|
|
scales = self._scaling.detach()
|
|
rotations = self._rotation.detach()
|
|
opacity = self._opacity.detach()
|
|
time = torch.tensor(0).to("cuda").repeat(means3D.shape[0],1)
|
|
means3D_deform, scales_deform, rotations_deform, _ = self._deformation(means3D, scales, rotations, opacity, time)
|
|
position_error = (means3D_deform - means3D)**2
|
|
rotation_error = (rotations_deform - rotations)**2
|
|
scaling_erorr = (scales_deform - scales)**2
|
|
return position_error.mean() + rotation_error.mean() + scaling_erorr.mean()
|
|
|
|
|
|
def add_densification_stats(self, viewspace_point_tensor, update_filter):
|
|
self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor[update_filter,:2], dim=-1, keepdim=True)
|
|
self.denom[update_filter] += 1
|
|
@torch.no_grad()
|
|
def update_deformation_table(self,threshold):
|
|
# print("origin deformation point nums:",self._deformation_table.sum())
|
|
self._deformation_table = torch.gt(self._deformation_accum.max(dim=-1).values/100,threshold)
|
|
def print_deformation_weight_grad(self):
|
|
for name, weight in self._deformation.named_parameters():
|
|
if weight.requires_grad:
|
|
if weight.grad is None:
|
|
|
|
print(name," :",weight.grad)
|
|
else:
|
|
if weight.grad.mean() != 0:
|
|
print(name," :",weight.grad.mean(), weight.grad.min(), weight.grad.max())
|
|
print("-"*50)
|
|
def _plane_regulation(self):
|
|
multi_res_grids = self._deformation.deformation_net.grid.grids
|
|
total = 0
|
|
# model.grids is 6 x [1, rank * F_dim, reso, reso]
|
|
for grids in multi_res_grids:
|
|
if len(grids) == 3:
|
|
time_grids = []
|
|
else:
|
|
time_grids = [0,1,3]
|
|
for grid_id in time_grids:
|
|
total += compute_plane_smoothness(grids[grid_id])
|
|
return total
|
|
def _time_regulation(self):
|
|
multi_res_grids = self._deformation.deformation_net.grid.grids
|
|
total = 0
|
|
# model.grids is 6 x [1, rank * F_dim, reso, reso]
|
|
for grids in multi_res_grids:
|
|
if len(grids) == 3:
|
|
time_grids = []
|
|
else:
|
|
time_grids =[2, 4, 5]
|
|
for grid_id in time_grids:
|
|
total += compute_plane_smoothness(grids[grid_id])
|
|
return total
|
|
def _l1_regulation(self):
|
|
# model.grids is 6 x [1, rank * F_dim, reso, reso]
|
|
multi_res_grids = self._deformation.deformation_net.grid.grids
|
|
|
|
total = 0.0
|
|
for grids in multi_res_grids:
|
|
if len(grids) == 3:
|
|
continue
|
|
else:
|
|
# These are the spatiotemporal grids
|
|
spatiotemporal_grids = [2, 4, 5]
|
|
for grid_id in spatiotemporal_grids:
|
|
total += torch.abs(1 - grids[grid_id]).mean()
|
|
return total
|
|
def compute_regulation(self, time_smoothness_weight, l1_time_planes_weight, plane_tv_weight):
|
|
return plane_tv_weight * self._plane_regulation() + time_smoothness_weight * self._time_regulation() + l1_time_planes_weight * self._l1_regulation()
|