# ORIGINAL LICENSE # SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: LicenseRef-NvidiaProprietary # # Modified by Jiale Xu # The modifications are subject to the same license as the original. import itertools import torch import torch.nn as nn from .utils.renderer import ImportanceRenderer from .utils.ray_sampler import RaySampler class OSGDecoder(nn.Module): """ Triplane decoder that gives RGB and sigma values from sampled features. Using ReLU here instead of Softplus in the original implementation. Reference: EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L112 """ def __init__(self, n_features: int, hidden_dim: int = 64, num_layers: int = 4, activation: nn.Module = nn.ReLU): super().__init__() self.net = nn.Sequential( nn.Linear(3 * n_features, hidden_dim), activation(), *itertools.chain(*[[ nn.Linear(hidden_dim, hidden_dim), activation(), ] for _ in range(num_layers - 2)]), nn.Linear(hidden_dim, 1 + 3), ) # init all bias to zero for m in self.modules(): if isinstance(m, nn.Linear): nn.init.zeros_(m.bias) def forward(self, sampled_features, ray_directions): # Aggregate features by mean # sampled_features = sampled_features.mean(1) # Aggregate features by concatenation _N, n_planes, _M, _C = sampled_features.shape sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C) x = sampled_features N, M, C = x.shape x = x.contiguous().view(N*M, C) x = self.net(x) x = x.view(N, M, -1) rgb = torch.sigmoid(x[..., 1:])*(1 + 2*0.001) - 0.001 # Uses sigmoid clamping from MipNeRF sigma = x[..., 0:1] return {'rgb': rgb, 'sigma': sigma} class TriplaneSynthesizer(nn.Module): """ Synthesizer that renders a triplane volume with planes and a camera. Reference: EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L19 """ DEFAULT_RENDERING_KWARGS = { 'ray_start': 'auto', 'ray_end': 'auto', 'box_warp': 2., 'white_back': True, 'disparity_space_sampling': False, 'clamp_mode': 'softplus', 'sampler_bbox_min': -1., 'sampler_bbox_max': 1., } def __init__(self, triplane_dim: int, samples_per_ray: int): super().__init__() # attributes self.triplane_dim = triplane_dim self.rendering_kwargs = { **self.DEFAULT_RENDERING_KWARGS, 'depth_resolution': samples_per_ray // 2, 'depth_resolution_importance': samples_per_ray // 2, } # renderings self.renderer = ImportanceRenderer() self.ray_sampler = RaySampler() # modules self.decoder = OSGDecoder(n_features=triplane_dim) def forward(self, planes, cameras, render_size=128, crop_params=None): # planes: (N, 3, D', H', W') # cameras: (N, M, D_cam) # render_size: int assert planes.shape[0] == cameras.shape[0], "Batch size mismatch for planes and cameras" N, M = cameras.shape[:2] cam2world_matrix = cameras[..., :16].view(N, M, 4, 4) intrinsics = cameras[..., 16:25].view(N, M, 3, 3) # Create a batch of rays for volume rendering ray_origins, ray_directions = self.ray_sampler( cam2world_matrix=cam2world_matrix.reshape(-1, 4, 4), intrinsics=intrinsics.reshape(-1, 3, 3), render_size=render_size, ) assert N*M == ray_origins.shape[0], "Batch size mismatch for ray_origins" assert ray_origins.dim() == 3, "ray_origins should be 3-dimensional" # Crop rays if crop_params is available if crop_params is not None: ray_origins = ray_origins.reshape(N*M, render_size, render_size, 3) ray_directions = ray_directions.reshape(N*M, render_size, render_size, 3) i, j, h, w = crop_params ray_origins = ray_origins[:, i:i+h, j:j+w, :].reshape(N*M, -1, 3) ray_directions = ray_directions[:, i:i+h, j:j+w, :].reshape(N*M, -1, 3) # Perform volume rendering rgb_samples, depth_samples, weights_samples = self.renderer( planes.repeat_interleave(M, dim=0), self.decoder, ray_origins, ray_directions, self.rendering_kwargs, ) # Reshape into 'raw' neural-rendered image if crop_params is not None: Himg, Wimg = crop_params[2:] else: Himg = Wimg = render_size rgb_images = rgb_samples.permute(0, 2, 1).reshape(N, M, rgb_samples.shape[-1], Himg, Wimg).contiguous() depth_images = depth_samples.permute(0, 2, 1).reshape(N, M, 1, Himg, Wimg) weight_images = weights_samples.permute(0, 2, 1).reshape(N, M, 1, Himg, Wimg) out = { 'images_rgb': rgb_images, 'images_depth': depth_images, 'images_weight': weight_images, } return out def forward_grid(self, planes, grid_size: int, aabb: torch.Tensor = None): # planes: (N, 3, D', H', W') # grid_size: int # aabb: (N, 2, 3) if aabb is None: aabb = torch.tensor([ [self.rendering_kwargs['sampler_bbox_min']] * 3, [self.rendering_kwargs['sampler_bbox_max']] * 3, ], device=planes.device, dtype=planes.dtype).unsqueeze(0).repeat(planes.shape[0], 1, 1) assert planes.shape[0] == aabb.shape[0], "Batch size mismatch for planes and aabb" N = planes.shape[0] # create grid points for triplane query grid_points = [] for i in range(N): grid_points.append(torch.stack(torch.meshgrid( torch.linspace(aabb[i, 0, 0], aabb[i, 1, 0], grid_size, device=planes.device), torch.linspace(aabb[i, 0, 1], aabb[i, 1, 1], grid_size, device=planes.device), torch.linspace(aabb[i, 0, 2], aabb[i, 1, 2], grid_size, device=planes.device), indexing='ij', ), dim=-1).reshape(-1, 3)) cube_grid = torch.stack(grid_points, dim=0).to(planes.device) features = self.forward_points(planes, cube_grid) # reshape into grid features = { k: v.reshape(N, grid_size, grid_size, grid_size, -1) for k, v in features.items() } return features def forward_points(self, planes, points: torch.Tensor, chunk_size: int = 2**20): # planes: (N, 3, D', H', W') # points: (N, P, 3) N, P = points.shape[:2] # query triplane in chunks outs = [] for i in range(0, points.shape[1], chunk_size): chunk_points = points[:, i:i+chunk_size] # query triplane chunk_out = self.renderer.run_model_activated( planes=planes, decoder=self.decoder, sample_coordinates=chunk_points, sample_directions=torch.zeros_like(chunk_points), options=self.rendering_kwargs, ) outs.append(chunk_out) # concatenate the outputs point_features = { k: torch.cat([out[k] for out in outs], dim=1) for k in outs[0].keys() } return point_features