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-----------------------------------------------------------------------------
Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.
-----------------------------------------------------------------------------
'''
import torch
from functools import partial
import numpy as np
import tinycudann as tcnn
from projects.neuralangelo.utils.spherical_harmonics import get_spherical_harmonics
from projects.neuralangelo.utils.mlp import MLPforNeuralSDF
from projects.neuralangelo.utils.misc import get_activation
from projects.nerf.utils import nerf_util
class NeuralSDF(torch.nn.Module):
def __init__(self, cfg_sdf):
super().__init__()
self.cfg_sdf = cfg_sdf
encoding_dim = self.build_encoding(cfg_sdf.encoding)
input_dim = 3 + encoding_dim
self.build_mlp(cfg_sdf.mlp, input_dim=input_dim)
def build_encoding(self, cfg_encoding):
if cfg_encoding.type == "fourier":
encoding_dim = 6 * cfg_encoding.levels
elif cfg_encoding.type == "hashgrid":
# Build the multi-resolution hash grid.
l_min, l_max = cfg_encoding.hashgrid.min_logres, cfg_encoding.hashgrid.max_logres
r_min, r_max = 2 ** l_min, 2 ** l_max
num_levels = cfg_encoding.levels
self.growth_rate = np.exp((np.log(r_max) - np.log(r_min)) / (num_levels - 1))
config = dict(
otype="HashGrid",
n_levels=cfg_encoding.levels,
n_features_per_level=cfg_encoding.hashgrid.dim,
log2_hashmap_size=cfg_encoding.hashgrid.dict_size,
base_resolution=2 ** cfg_encoding.hashgrid.min_logres,
per_level_scale=self.growth_rate,
)
self.tcnn_encoding = tcnn.Encoding(3, config)
self.resolutions = []
for lv in range(0, num_levels):
size = np.floor(r_min * self.growth_rate ** lv).astype(int) + 1
self.resolutions.append(size)
encoding_dim = cfg_encoding.hashgrid.dim * cfg_encoding.levels
else:
raise NotImplementedError("Unknown encoding type")
return encoding_dim
def build_mlp(self, cfg_mlp, input_dim=3):
# SDF + point-wise feature
layer_dims = [input_dim] + [cfg_mlp.hidden_dim] * cfg_mlp.num_layers
activ = get_activation(cfg_mlp.activ, **cfg_mlp.activ_params)
self.mlp = MLPforNeuralSDF(layer_dims, skip_connection=cfg_mlp.skip, activ=activ,
use_weightnorm=cfg_mlp.weight_norm, geometric_init=cfg_mlp.geometric_init,
out_bias=cfg_mlp.out_bias, invert=cfg_mlp.inside_out)
def forward(self, points_3D, with_sdf=True, with_feat=True):
points_enc = self.encode(points_3D) # [...,3+LD]
sdf, feat = self.mlp(points_enc, with_sdf=with_sdf, with_feat=with_feat)
return sdf, feat # [...,1],[...,K]
def sdf(self, points_3D):
return self.forward(points_3D, with_sdf=True, with_feat=False)[0]
def encode(self, points_3D):
if self.cfg_sdf.encoding.type == "fourier":
points_enc = nerf_util.positional_encoding(points_3D, num_freq_bases=self.cfg_sdf.encoding.levels)
feat_dim = 6
elif self.cfg_sdf.encoding.type == "hashgrid":
# Tri-linear interpolate the corresponding embeddings from the dictionary.
vol_min, vol_max = self.cfg_sdf.encoding.hashgrid.range
points_3D_normalized = (points_3D - vol_min) / (vol_max - vol_min) # Normalize to [0,1].
tcnn_input = points_3D_normalized.view(-1, 3)
tcnn_output = self.tcnn_encoding(tcnn_input)
points_enc = tcnn_output.view(*points_3D_normalized.shape[:-1], tcnn_output.shape[-1])
feat_dim = self.cfg_sdf.encoding.hashgrid.dim
else:
raise NotImplementedError("Unknown encoding type")
# Coarse-to-fine.
if self.cfg_sdf.encoding.coarse2fine.enabled:
mask = self._get_coarse2fine_mask(points_enc, feat_dim=feat_dim)
points_enc = points_enc * mask
points_enc = torch.cat([points_3D, points_enc], dim=-1) # [B,R,N,3+LD]
return points_enc
def set_active_levels(self, current_iter=None):
add_levels = (current_iter - self.warm_up_end) // self.cfg_sdf.encoding.coarse2fine.step
self.add_levels = min(self.cfg_sdf.encoding.levels - self.cfg_sdf.encoding.coarse2fine.init_active_level,
add_levels)
self.active_levels = self.cfg_sdf.encoding.coarse2fine.init_active_level + self.add_levels
assert self.active_levels <= self.cfg_sdf.encoding.levels
def set_normal_epsilon(self):
epsilon_res = self.resolutions[self.active_levels - 1]
self.normal_eps = 1. / epsilon_res
@torch.no_grad()
def _get_coarse2fine_mask(self, points_enc, feat_dim):
mask = torch.zeros_like(points_enc)
mask[..., :(self.active_levels * feat_dim)] = 1
return mask
def compute_gradients(self, x, compute_hessian=False, sdf=None):
# Note: hessian is not fully hessian but diagonal elements
if self.cfg_sdf.gradient.mode == "analytical":
requires_grad = x.requires_grad
with torch.enable_grad():
# 1st-order gradient
x.requires_grad_(True)
y = self.sdf(x)
gradient = torch.autograd.grad(y.sum(), x, create_graph=True)[0]
# 2nd-order gradient (hessian)
if compute_hessian:
hessian = torch.autograd.grad(gradient.sum(), x, create_graph=True)[0]
else:
hessian = None
x.requires_grad_(requires_grad)
if not requires_grad:
gradient = gradient.detach()
if compute_hessian:
hessian = hessian.detach()
elif self.cfg_sdf.gradient.mode == "numerical":
eps = self.normal_eps
if self.cfg_sdf.gradient.taps == 6:
# 1st-order gradient
eps_x = torch.tensor([eps, 0., 0.], dtype=x.dtype, device=x.device) # [3]
eps_y = torch.tensor([0., eps, 0.], dtype=x.dtype, device=x.device) # [3]
eps_z = torch.tensor([0., 0., eps], dtype=x.dtype, device=x.device) # [3]
sdf_x_pos = self.sdf(x + eps_x) # [...,1]
sdf_x_neg = self.sdf(x - eps_x) # [...,1]
sdf_y_pos = self.sdf(x + eps_y) # [...,1]
sdf_y_neg = self.sdf(x - eps_y) # [...,1]
sdf_z_pos = self.sdf(x + eps_z) # [...,1]
sdf_z_neg = self.sdf(x - eps_z) # [...,1]
gradient_x = (sdf_x_pos - sdf_x_neg) / (2 * eps)
gradient_y = (sdf_y_pos - sdf_y_neg) / (2 * eps)
gradient_z = (sdf_z_pos - sdf_z_neg) / (2 * eps)
gradient = torch.cat([gradient_x, gradient_y, gradient_z], dim=-1) # [...,3]
# 2nd-order gradient (hessian)
if compute_hessian:
assert sdf is not None # computed when feed-forwarding through the network
hessian_xx = (sdf_x_pos + sdf_x_neg - 2 * sdf) / (eps ** 2) # [...,1]
hessian_yy = (sdf_y_pos + sdf_y_neg - 2 * sdf) / (eps ** 2) # [...,1]
hessian_zz = (sdf_z_pos + sdf_z_neg - 2 * sdf) / (eps ** 2) # [...,1]
hessian = torch.cat([hessian_xx, hessian_yy, hessian_zz], dim=-1) # [...,3]
else:
hessian = None
elif self.cfg_sdf.gradient.taps == 4:
k1 = torch.tensor([1, -1, -1], dtype=x.dtype, device=x.device) # [3]
k2 = torch.tensor([-1, -1, 1], dtype=x.dtype, device=x.device) # [3]
k3 = torch.tensor([-1, 1, -1], dtype=x.dtype, device=x.device) # [3]
k4 = torch.tensor([1, 1, 1], dtype=x.dtype, device=x.device) # [3]
sdf1 = self.sdf(x + k1 * eps) # [...,1]
sdf2 = self.sdf(x + k2 * eps) # [...,1]
sdf3 = self.sdf(x + k3 * eps) # [...,1]
sdf4 = self.sdf(x + k4 * eps) # [...,1]
gradient = (k1*sdf1 + k2*sdf2 + k3*sdf3 + k4*sdf4) / (4.0 * eps)
if compute_hessian:
# the result of 4 taps is directly trace, but we assume they are individual components
# so we use the same signature as 6 taps
hessian_xx = ((sdf1 + sdf2 + sdf3 + sdf4) / 2.0 - 2 * sdf) / eps ** 2 # [N,1]
hessian = torch.cat([hessian_xx, hessian_xx, hessian_xx], dim=-1) / 3.0
else:
hessian = None
else:
raise ValueError("Only support 4 or 6 taps.")
return gradient, hessian
class NeuralRGB(torch.nn.Module):
def __init__(self, cfg_rgb, feat_dim, appear_embed):
super().__init__()
self.cfg_rgb = cfg_rgb
self.cfg_appear_embed = appear_embed
encoding_view_dim = self.build_encoding(cfg_rgb.encoding_view)
input_base_dim = 6 if cfg_rgb.mode == "idr" else 3
input_dim = input_base_dim + encoding_view_dim + feat_dim + (appear_embed.dim if appear_embed.enabled else 0)
self.build_mlp(cfg_rgb.mlp, input_dim=input_dim)
def build_encoding(self, cfg_encoding_view):
if cfg_encoding_view.type == "fourier":
encoding_view_dim = 6 * cfg_encoding_view.levels
elif cfg_encoding_view.type == "spherical":
self.spherical_harmonic_encoding = partial(get_spherical_harmonics, levels=cfg_encoding_view.levels)
encoding_view_dim = (cfg_encoding_view.levels + 1) ** 2
else:
raise NotImplementedError("Unknown encoding type")
return encoding_view_dim
def build_mlp(self, cfg_mlp, input_dim=3):
# RGB prediction
layer_dims = [input_dim] + [cfg_mlp.hidden_dim] * cfg_mlp.num_layers + [3]
activ = get_activation(cfg_mlp.activ, **cfg_mlp.activ_params)
self.mlp = nerf_util.MLPwithSkipConnection(layer_dims, skip_connection=cfg_mlp.skip, activ=activ,
use_weightnorm=cfg_mlp.weight_norm)
def forward(self, points_3D, normals, rays_unit, feats, app):
view_enc = self.encode_view(rays_unit) # [...,LD]
input_list = [points_3D, view_enc, normals, feats]
if app is not None:
input_list.append(app)
if self.cfg_rgb.mode == "no_view_dir":
input_list.remove(view_enc)
if self.cfg_rgb.mode == "no_normal":
input_list.remove(normals)
input_vec = torch.cat(input_list, dim=-1)
rgb = self.mlp(input_vec).sigmoid_()
return rgb # [...,3]
def encode_view(self, rays_unit):
if self.cfg_rgb.encoding_view.type == "fourier":
view_enc = nerf_util.positional_encoding(rays_unit, num_freq_bases=self.cfg_rgb.encoding_view.levels)
elif self.cfg_rgb.encoding_view.type == "spherical":
view_enc = self.spherical_harmonic_encoding(rays_unit)
else:
raise NotImplementedError("Unknown encoding type")
return view_enc
class BackgroundNeRF(torch.nn.Module):
def __init__(self, cfg_background, appear_embed):
super().__init__()
self.cfg_background = cfg_background
self.cfg_appear_embed = appear_embed
encoding_dim, encoding_view_dim = self.build_encoding(cfg_background.encoding, cfg_background.encoding_view)
input_dim = 4 + encoding_dim
input_view_dim = cfg_background.mlp.hidden_dim + encoding_view_dim + \
(appear_embed.dim if appear_embed.enabled else 0)
self.build_mlp(cfg_background.mlp, input_dim=input_dim, input_view_dim=input_view_dim)
def build_encoding(self, cfg_encoding, cfg_encoding_view):
# Positional encoding.
if cfg_encoding.type == "fourier":
encoding_dim = 8 * cfg_encoding.levels
else:
raise NotImplementedError("Unknown encoding type")
# View encoding.
if cfg_encoding_view.type == "fourier":
encoding_view_dim = 6 * cfg_encoding_view.levels
elif cfg_encoding_view.type == "spherical":
self.spherical_harmonic_encoding = partial(get_spherical_harmonics, levels=cfg_encoding_view.levels)
encoding_view_dim = (cfg_encoding_view.levels + 1) ** 2
else:
raise NotImplementedError("Unknown encoding type")
return encoding_dim, encoding_view_dim
def build_mlp(self, cfg_mlp, input_dim=3, input_view_dim=3):
activ = get_activation(cfg_mlp.activ, **cfg_mlp.activ_params)
# Point-wise feature.
layer_dims = [input_dim] + [cfg_mlp.hidden_dim] * (cfg_mlp.num_layers - 1) + [cfg_mlp.hidden_dim + 1]
self.mlp_feat = nerf_util.MLPwithSkipConnection(layer_dims, skip_connection=cfg_mlp.skip, activ=activ)
self.activ_density = get_activation(cfg_mlp.activ_density, **cfg_mlp.activ_density_params)
# RGB prediction.
layer_dims_rgb = [input_view_dim] + [cfg_mlp.hidden_dim_rgb] * (cfg_mlp.num_layers_rgb - 1) + [3]
self.mlp_rgb = nerf_util.MLPwithSkipConnection(layer_dims_rgb, skip_connection=cfg_mlp.skip_rgb, activ=activ)
def forward(self, points_3D, rays_unit, app_outside):
points_enc = self.encode(points_3D) # [...,4+LD]
# Volume density prediction.
out = self.mlp_feat(points_enc)
density, feat = self.activ_density(out[..., 0]), self.mlp_feat.activ(out[..., 1:]) # [...],[...,K]
# RGB color prediction.
if self.cfg_background.view_dep:
view_enc = self.encode_view(rays_unit) # [...,LD]
input_list = [feat, view_enc]
if app_outside is not None:
input_list.append(app_outside)
input_vec = torch.cat(input_list, dim=-1)
rgb = self.mlp_rgb(input_vec).sigmoid_() # [...,3]
else:
raise NotImplementedError
return rgb, density
def encode(self, points_3D):
# Reparametrize the 3D points.
# TODO: revive this.
if True:
points_3D_norm = points_3D.norm(dim=-1, keepdim=True) # [B,R,N,1]
points = torch.cat([points_3D / points_3D_norm, 1.0 / points_3D_norm], dim=-1) # [B,R,N,4]
else:
points = points_3D
# Positional encoding.
if self.cfg_background.encoding.type == "fourier":
points_enc = nerf_util.positional_encoding(points, num_freq_bases=self.cfg_background.encoding.levels)
else:
raise NotImplementedError("Unknown encoding type")
# TODO: 1/x?
points_enc = torch.cat([points, points_enc], dim=-1) # [B,R,N,4+LD]
return points_enc
def encode_view(self, rays_unit):
if self.cfg_background.encoding_view.type == "fourier":
view_enc = nerf_util.positional_encoding(rays_unit, num_freq_bases=self.cfg_background.encoding_view.levels)
elif self.cfg_background.encoding_view.type == "spherical":
view_enc = self.spherical_harmonic_encoding(rays_unit)
else:
raise NotImplementedError("Unknown encoding type")
return view_enc
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