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import os | |
import math | |
import numpy as np | |
from typing import NamedTuple | |
from plyfile import PlyData, PlyElement | |
import torch | |
from torch import nn | |
from diff_gaussian_rasterization import ( | |
GaussianRasterizationSettings, | |
GaussianRasterizer, | |
) | |
from simple_knn._C import distCUDA2 | |
from sh_utils import eval_sh, SH2RGB, RGB2SH | |
from mesh import Mesh | |
from mesh_utils import decimate_mesh, clean_mesh | |
import kiui | |
def inverse_sigmoid(x): | |
return torch.log(x/(1-x)) | |
def get_expon_lr_func( | |
lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000 | |
): | |
def helper(step): | |
if lr_init == lr_final: | |
# constant lr, ignore other params | |
return lr_init | |
if step < 0 or (lr_init == 0.0 and lr_final == 0.0): | |
# Disable this parameter | |
return 0.0 | |
if lr_delay_steps > 0: | |
# A kind of reverse cosine decay. | |
delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin( | |
0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1) | |
) | |
else: | |
delay_rate = 1.0 | |
t = np.clip(step / max_steps, 0, 1) | |
log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) | |
return delay_rate * log_lerp | |
return helper | |
def strip_lowerdiag(L): | |
uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda") | |
uncertainty[:, 0] = L[:, 0, 0] | |
uncertainty[:, 1] = L[:, 0, 1] | |
uncertainty[:, 2] = L[:, 0, 2] | |
uncertainty[:, 3] = L[:, 1, 1] | |
uncertainty[:, 4] = L[:, 1, 2] | |
uncertainty[:, 5] = L[:, 2, 2] | |
return uncertainty | |
def strip_symmetric(sym): | |
return strip_lowerdiag(sym) | |
def gaussian_3d_coeff(xyzs, covs): | |
# xyzs: [N, 3] | |
# covs: [N, 6] | |
x, y, z = xyzs[:, 0], xyzs[:, 1], xyzs[:, 2] | |
a, b, c, d, e, f = covs[:, 0], covs[:, 1], covs[:, 2], covs[:, 3], covs[:, 4], covs[:, 5] | |
# eps must be small enough !!! | |
inv_det = 1 / (a * d * f + 2 * e * c * b - e**2 * a - c**2 * d - b**2 * f + 1e-24) | |
inv_a = (d * f - e**2) * inv_det | |
inv_b = (e * c - b * f) * inv_det | |
inv_c = (e * b - c * d) * inv_det | |
inv_d = (a * f - c**2) * inv_det | |
inv_e = (b * c - e * a) * inv_det | |
inv_f = (a * d - b**2) * inv_det | |
power = -0.5 * (x**2 * inv_a + y**2 * inv_d + z**2 * inv_f) - x * y * inv_b - x * z * inv_c - y * z * inv_e | |
power[power > 0] = -1e10 # abnormal values... make weights 0 | |
return torch.exp(power) | |
def build_rotation(r): | |
norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3]) | |
q = r / norm[:, None] | |
R = torch.zeros((q.size(0), 3, 3), device='cuda') | |
r = q[:, 0] | |
x = q[:, 1] | |
y = q[:, 2] | |
z = q[:, 3] | |
R[:, 0, 0] = 1 - 2 * (y*y + z*z) | |
R[:, 0, 1] = 2 * (x*y - r*z) | |
R[:, 0, 2] = 2 * (x*z + r*y) | |
R[:, 1, 0] = 2 * (x*y + r*z) | |
R[:, 1, 1] = 1 - 2 * (x*x + z*z) | |
R[:, 1, 2] = 2 * (y*z - r*x) | |
R[:, 2, 0] = 2 * (x*z - r*y) | |
R[:, 2, 1] = 2 * (y*z + r*x) | |
R[:, 2, 2] = 1 - 2 * (x*x + y*y) | |
return R | |
def build_scaling_rotation(s, r): | |
L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda") | |
R = build_rotation(r) | |
L[:,0,0] = s[:,0] | |
L[:,1,1] = s[:,1] | |
L[:,2,2] = s[:,2] | |
L = R @ L | |
return L | |
class BasicPointCloud(NamedTuple): | |
points: np.array | |
colors: np.array | |
normals: np.array | |
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._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.setup_functions() | |
def capture(self): | |
return ( | |
self.active_sh_degree, | |
self._xyz, | |
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, | |
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.training_setup(training_args) | |
self.xyz_gradient_accum = xyz_gradient_accum | |
self.denom = denom | |
self.optimizer.load_state_dict(opt_dict) | |
def get_scaling(self): | |
return self.scaling_activation(self._scaling) | |
def get_rotation(self): | |
return self.rotation_activation(self._rotation) | |
def get_xyz(self): | |
return self._xyz | |
def get_features(self): | |
features_dc = self._features_dc | |
features_rest = self._features_rest | |
return torch.cat((features_dc, features_rest), dim=1) | |
def get_opacity(self): | |
return self.opacity_activation(self._opacity) | |
def extract_fields(self, resolution=128, num_blocks=16, relax_ratio=1.5): | |
# resolution: resolution of field | |
block_size = 2 / num_blocks | |
assert resolution % block_size == 0 | |
split_size = resolution // num_blocks | |
opacities = self.get_opacity | |
# pre-filter low opacity gaussians to save computation | |
mask = (opacities > 0.005).squeeze(1) | |
opacities = opacities[mask] | |
xyzs = self.get_xyz[mask] | |
stds = self.get_scaling[mask] | |
# normalize to ~ [-1, 1] | |
mn, mx = xyzs.amin(0), xyzs.amax(0) | |
self.center = (mn + mx) / 2 | |
self.scale = 1.8 / (mx - mn).amax().item() | |
xyzs = (xyzs - self.center) * self.scale | |
stds = stds * self.scale | |
covs = self.covariance_activation(stds, 1, self._rotation[mask]) | |
# tile | |
device = opacities.device | |
occ = torch.zeros([resolution] * 3, dtype=torch.float32, device=device) | |
X = torch.linspace(-1, 1, resolution).split(split_size) | |
Y = torch.linspace(-1, 1, resolution).split(split_size) | |
Z = torch.linspace(-1, 1, resolution).split(split_size) | |
# loop blocks (assume max size of gaussian is small than relax_ratio * block_size !!!) | |
for xi, xs in enumerate(X): | |
for yi, ys in enumerate(Y): | |
for zi, zs in enumerate(Z): | |
xx, yy, zz = torch.meshgrid(xs, ys, zs) | |
# sample points [M, 3] | |
pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1).to(device) | |
# in-tile gaussians mask | |
vmin, vmax = pts.amin(0), pts.amax(0) | |
vmin -= block_size * relax_ratio | |
vmax += block_size * relax_ratio | |
mask = (xyzs < vmax).all(-1) & (xyzs > vmin).all(-1) | |
# if hit no gaussian, continue to next block | |
if not mask.any(): | |
continue | |
mask_xyzs = xyzs[mask] # [L, 3] | |
mask_covs = covs[mask] # [L, 6] | |
mask_opas = opacities[mask].view(1, -1) # [L, 1] --> [1, L] | |
# query per point-gaussian pair. | |
g_pts = pts.unsqueeze(1).repeat(1, mask_covs.shape[0], 1) - mask_xyzs.unsqueeze(0) # [M, L, 3] | |
g_covs = mask_covs.unsqueeze(0).repeat(pts.shape[0], 1, 1) # [M, L, 6] | |
# batch on gaussian to avoid OOM | |
batch_g = 1024 | |
val = 0 | |
for start in range(0, g_covs.shape[1], batch_g): | |
end = min(start + batch_g, g_covs.shape[1]) | |
w = gaussian_3d_coeff(g_pts[:, start:end].reshape(-1, 3), g_covs[:, start:end].reshape(-1, 6)).reshape(pts.shape[0], -1) # [M, l] | |
val += (mask_opas[:, start:end] * w).sum(-1) | |
# kiui.lo(val, mask_opas, w) | |
occ[xi * split_size: xi * split_size + len(xs), | |
yi * split_size: yi * split_size + len(ys), | |
zi * split_size: zi * split_size + len(zs)] = val.reshape(len(xs), len(ys), len(zs)) | |
kiui.lo(occ, verbose=1) | |
return occ | |
def extract_mesh(self, path, density_thresh=1, resolution=128, decimate_target=1e5): | |
os.makedirs(os.path.dirname(path), exist_ok=True) | |
occ = self.extract_fields(resolution).detach().cpu().numpy() | |
import mcubes | |
vertices, triangles = mcubes.marching_cubes(occ, density_thresh) | |
vertices = vertices / (resolution - 1.0) * 2 - 1 | |
# transform back to the original space | |
vertices = vertices / self.scale + self.center.detach().cpu().numpy() | |
vertices, triangles = clean_mesh(vertices, triangles, remesh=True, remesh_size=0.015) | |
if decimate_target > 0 and triangles.shape[0] > decimate_target: | |
vertices, triangles = decimate_mesh(vertices, triangles, decimate_target) | |
v = torch.from_numpy(vertices.astype(np.float32)).contiguous().cuda() | |
f = torch.from_numpy(triangles.astype(np.int32)).contiguous().cuda() | |
print( | |
f"[INFO] marching cubes result: {v.shape} ({v.min().item()}-{v.max().item()}), {f.shape}" | |
) | |
mesh = Mesh(v=v, f=f, device='cuda') | |
return mesh | |
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 = 1): | |
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]) | |
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._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") | |
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") | |
l = [ | |
{'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"}, | |
{'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) | |
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 | |
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 save_ply(self, path): | |
os.makedirs(os.path.dirname(path), exist_ok=True) | |
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] | |
print("Number of points at loading : ", xyz.shape[0]) | |
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_")] | |
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_")] | |
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")] | |
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: | |
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.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: | |
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): | |
d = {"xyz": new_xyz, | |
"f_dc": new_features_dc, | |
"f_rest": new_features_rest, | |
"opacity": new_opacities, | |
"scaling" : new_scaling, | |
"rotation" : new_rotation} | |
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.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)) | |
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) | |
self.densification_postfix(new_xyz, new_features_dc, new_features_rest, 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_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_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 > 0.1 * extent | |
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 prune(self, 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(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 getProjectionMatrix(znear, zfar, fovX, fovY): | |
tanHalfFovY = math.tan((fovY / 2)) | |
tanHalfFovX = math.tan((fovX / 2)) | |
P = torch.zeros(4, 4) | |
z_sign = 1.0 | |
P[0, 0] = 1 / tanHalfFovX | |
P[1, 1] = 1 / tanHalfFovY | |
P[3, 2] = z_sign | |
P[2, 2] = z_sign * zfar / (zfar - znear) | |
P[2, 3] = -(zfar * znear) / (zfar - znear) | |
return P | |
class MiniCam: | |
def __init__(self, c2w, width, height, fovy, fovx, znear, zfar): | |
# c2w (pose) should be in NeRF convention. | |
self.image_width = width | |
self.image_height = height | |
self.FoVy = fovy | |
self.FoVx = fovx | |
self.znear = znear | |
self.zfar = zfar | |
w2c = np.linalg.inv(c2w) | |
# rectify... | |
w2c[1:3, :3] *= -1 | |
w2c[:3, 3] *= -1 | |
self.world_view_transform = torch.tensor(w2c).transpose(0, 1).cuda() | |
self.projection_matrix = ( | |
getProjectionMatrix( | |
znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy | |
) | |
.transpose(0, 1) | |
.cuda() | |
) | |
self.full_proj_transform = self.world_view_transform @ self.projection_matrix | |
self.camera_center = -torch.tensor(c2w[:3, 3]).cuda() | |
class Renderer: | |
def __init__(self, sh_degree=3, white_background=True, radius=1): | |
self.sh_degree = sh_degree | |
self.white_background = white_background | |
self.radius = radius | |
self.gaussians = GaussianModel(sh_degree) | |
self.bg_color = torch.tensor( | |
[1, 1, 1] if white_background else [0, 0, 0], | |
dtype=torch.float32, | |
device="cuda", | |
) | |
def initialize(self, input=None, num_pts=5000, radius=0.5): | |
# load checkpoint | |
if input is None: | |
# init from random point cloud | |
phis = np.random.random((num_pts,)) * 2 * np.pi | |
costheta = np.random.random((num_pts,)) * 2 - 1 | |
thetas = np.arccos(costheta) | |
mu = np.random.random((num_pts,)) | |
radius = radius * np.cbrt(mu) | |
x = radius * np.sin(thetas) * np.cos(phis) | |
y = radius * np.sin(thetas) * np.sin(phis) | |
z = radius * np.cos(thetas) | |
xyz = np.stack((x, y, z), axis=1) | |
# xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3 | |
shs = np.random.random((num_pts, 3)) / 255.0 | |
pcd = BasicPointCloud( | |
points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)) | |
) | |
self.gaussians.create_from_pcd(pcd, 10) | |
elif isinstance(input, BasicPointCloud): | |
# load from a provided pcd | |
self.gaussians.create_from_pcd(input, 1) | |
else: | |
# load from saved ply | |
self.gaussians.load_ply(input) | |
def render( | |
self, | |
viewpoint_camera, | |
scaling_modifier=1.0, | |
invert_bg_color=False, | |
override_color=None, | |
compute_cov3D_python=False, | |
convert_SHs_python=False, | |
): | |
# Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means | |
screenspace_points = ( | |
torch.zeros_like( | |
self.gaussians.get_xyz, | |
dtype=self.gaussians.get_xyz.dtype, | |
requires_grad=True, | |
device="cuda", | |
) | |
+ 0 | |
) | |
try: | |
screenspace_points.retain_grad() | |
except: | |
pass | |
# Set up rasterization configuration | |
tanfovx = math.tan(viewpoint_camera.FoVx * 0.5) | |
tanfovy = math.tan(viewpoint_camera.FoVy * 0.5) | |
raster_settings = GaussianRasterizationSettings( | |
image_height=int(viewpoint_camera.image_height), | |
image_width=int(viewpoint_camera.image_width), | |
tanfovx=tanfovx, | |
tanfovy=tanfovy, | |
bg=self.bg_color if not invert_bg_color else 1 - self.bg_color, | |
scale_modifier=scaling_modifier, | |
viewmatrix=viewpoint_camera.world_view_transform, | |
projmatrix=viewpoint_camera.full_proj_transform, | |
sh_degree=self.gaussians.active_sh_degree, | |
campos=viewpoint_camera.camera_center, | |
prefiltered=False, | |
debug=False, | |
) | |
rasterizer = GaussianRasterizer(raster_settings=raster_settings) | |
means3D = self.gaussians.get_xyz | |
means2D = screenspace_points | |
opacity = self.gaussians.get_opacity | |
# If precomputed 3d covariance is provided, use it. If not, then it will be computed from | |
# scaling / rotation by the rasterizer. | |
scales = None | |
rotations = None | |
cov3D_precomp = None | |
if compute_cov3D_python: | |
cov3D_precomp = self.gaussians.get_covariance(scaling_modifier) | |
else: | |
scales = self.gaussians.get_scaling | |
rotations = self.gaussians.get_rotation | |
# If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors | |
# from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer. | |
shs = None | |
colors_precomp = None | |
if colors_precomp is None: | |
if convert_SHs_python: | |
shs_view = self.gaussians.get_features.transpose(1, 2).view( | |
-1, 3, (self.gaussians.max_sh_degree + 1) ** 2 | |
) | |
dir_pp = self.gaussians.get_xyz - viewpoint_camera.camera_center.repeat( | |
self.gaussians.get_features.shape[0], 1 | |
) | |
dir_pp_normalized = dir_pp / dir_pp.norm(dim=1, keepdim=True) | |
sh2rgb = eval_sh( | |
self.gaussians.active_sh_degree, shs_view, dir_pp_normalized | |
) | |
colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0) | |
else: | |
shs = self.gaussians.get_features | |
else: | |
colors_precomp = override_color | |
# Rasterize visible Gaussians to image, obtain their radii (on screen). | |
rendered_image, radii, rendered_depth, rendered_alpha = rasterizer( | |
means3D=means3D, | |
means2D=means2D, | |
shs=shs, | |
colors_precomp=colors_precomp, | |
opacities=opacity, | |
scales=scales, | |
rotations=rotations, | |
cov3D_precomp=cov3D_precomp, | |
) | |
rendered_image = rendered_image.clamp(0, 1) | |
# Those Gaussians that were frustum culled or had a radius of 0 were not visible. | |
# They will be excluded from value updates used in the splitting criteria. | |
return { | |
"image": rendered_image, | |
"depth": rendered_depth, | |
"alpha": rendered_alpha, | |
"viewspace_points": screenspace_points, | |
"visibility_filter": radii > 0, | |
"radii": radii, | |
} | |