Spaces:
Running
on
Zero
Running
on
Zero
| # SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: LicenseRef-NvidiaProprietary | |
| # | |
| # NVIDIA CORPORATION, its affiliates and licensors retain all intellectual | |
| # property and proprietary rights in and to this material, related | |
| # documentation and any modifications thereto. Any use, reproduction, | |
| # disclosure or distribution of this material and related documentation | |
| # without an express license agreement from NVIDIA CORPORATION or | |
| # its affiliates is strictly prohibited. | |
| # | |
| # Modified by Jiale Xu | |
| # The modifications are subject to the same license as the original. | |
| """ | |
| The ray sampler is a module that takes in camera matrices and resolution and batches of rays. | |
| Expects cam2world matrices that use the OpenCV camera coordinate system conventions. | |
| """ | |
| import torch | |
| class RaySampler(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None | |
| def forward(self, cam2world_matrix, intrinsics, render_size): | |
| """ | |
| Create batches of rays and return origins and directions. | |
| cam2world_matrix: (N, 4, 4) | |
| intrinsics: (N, 3, 3) | |
| render_size: int | |
| ray_origins: (N, M, 3) | |
| ray_dirs: (N, M, 2) | |
| """ | |
| dtype = cam2world_matrix.dtype | |
| device = cam2world_matrix.device | |
| N, M = cam2world_matrix.shape[0], render_size**2 | |
| cam_locs_world = cam2world_matrix[:, :3, 3] | |
| fx = intrinsics[:, 0, 0] | |
| fy = intrinsics[:, 1, 1] | |
| cx = intrinsics[:, 0, 2] | |
| cy = intrinsics[:, 1, 2] | |
| sk = intrinsics[:, 0, 1] | |
| uv = torch.stack(torch.meshgrid( | |
| torch.arange(render_size, dtype=dtype, device=device), | |
| torch.arange(render_size, dtype=dtype, device=device), | |
| indexing='ij', | |
| )) | |
| uv = uv.flip(0).reshape(2, -1).transpose(1, 0) | |
| uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1) | |
| x_cam = uv[:, :, 0].view(N, -1) * (1./render_size) + (0.5/render_size) | |
| y_cam = uv[:, :, 1].view(N, -1) * (1./render_size) + (0.5/render_size) | |
| z_cam = torch.ones((N, M), dtype=dtype, device=device) | |
| x_lift = (x_cam - cx.unsqueeze(-1) + cy.unsqueeze(-1)*sk.unsqueeze(-1)/fy.unsqueeze(-1) - sk.unsqueeze(-1)*y_cam/fy.unsqueeze(-1)) / fx.unsqueeze(-1) * z_cam | |
| y_lift = (y_cam - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z_cam | |
| cam_rel_points = torch.stack((x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1).to(dtype) | |
| _opencv2blender = torch.tensor([ | |
| [1, 0, 0, 0], | |
| [0, -1, 0, 0], | |
| [0, 0, -1, 0], | |
| [0, 0, 0, 1], | |
| ], dtype=dtype, device=device).unsqueeze(0).repeat(N, 1, 1) | |
| cam2world_matrix = torch.bmm(cam2world_matrix, _opencv2blender) | |
| world_rel_points = torch.bmm(cam2world_matrix, cam_rel_points.permute(0, 2, 1)).permute(0, 2, 1)[:, :, :3] | |
| ray_dirs = world_rel_points - cam_locs_world[:, None, :] | |
| ray_dirs = torch.nn.functional.normalize(ray_dirs, dim=2).to(dtype) | |
| ray_origins = cam_locs_world.unsqueeze(1).repeat(1, ray_dirs.shape[1], 1) | |
| return ray_origins, ray_dirs | |
| class OrthoRaySampler(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None | |
| def forward(self, cam2world_matrix, ortho_scale, render_size): | |
| """ | |
| Create batches of rays and return origins and directions. | |
| cam2world_matrix: (N, 4, 4) | |
| ortho_scale: float | |
| render_size: int | |
| ray_origins: (N, M, 3) | |
| ray_dirs: (N, M, 3) | |
| """ | |
| N, M = cam2world_matrix.shape[0], render_size**2 | |
| uv = torch.stack(torch.meshgrid( | |
| torch.arange(render_size, dtype=torch.float32, device=cam2world_matrix.device), | |
| torch.arange(render_size, dtype=torch.float32, device=cam2world_matrix.device), | |
| indexing='ij', | |
| )) | |
| uv = uv.flip(0).reshape(2, -1).transpose(1, 0) | |
| uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1) | |
| x_cam = uv[:, :, 0].view(N, -1) * (1./render_size) + (0.5/render_size) | |
| y_cam = uv[:, :, 1].view(N, -1) * (1./render_size) + (0.5/render_size) | |
| z_cam = torch.zeros((N, M), device=cam2world_matrix.device) | |
| x_lift = (x_cam - 0.5) * ortho_scale | |
| y_lift = (y_cam - 0.5) * ortho_scale | |
| cam_rel_points = torch.stack((x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1) | |
| _opencv2blender = torch.tensor([ | |
| [1, 0, 0, 0], | |
| [0, -1, 0, 0], | |
| [0, 0, -1, 0], | |
| [0, 0, 0, 1], | |
| ], dtype=torch.float32, device=cam2world_matrix.device).unsqueeze(0).repeat(N, 1, 1) | |
| cam2world_matrix = torch.bmm(cam2world_matrix, _opencv2blender) | |
| ray_origins = torch.bmm(cam2world_matrix, cam_rel_points.permute(0, 2, 1)).permute(0, 2, 1)[:, :, :3] | |
| ray_dirs_cam = torch.stack([ | |
| torch.zeros((N, M), device=cam2world_matrix.device), | |
| torch.zeros((N, M), device=cam2world_matrix.device), | |
| torch.ones((N, M), device=cam2world_matrix.device), | |
| ], dim=-1) | |
| ray_dirs = torch.bmm(cam2world_matrix[:, :3, :3], ray_dirs_cam.permute(0, 2, 1)).permute(0, 2, 1) | |
| return ray_origins, ray_dirs | |