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| # 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. | |
| """ | |
| 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, resolution): | |
| """ | |
| Create batches of rays and return origins and directions. | |
| cam2world_matrix: (N, 4, 4) | |
| intrinsics: (N, 3, 3) | |
| resolution: int | |
| ray_origins: (N, M, 3) | |
| ray_dirs: (N, M, 2) | |
| """ | |
| N, M = cam2world_matrix.shape[0], resolution**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(resolution, dtype=torch.float32, device=cam2world_matrix.device), | |
| torch.arange(resolution, dtype=torch.float32, device=cam2world_matrix.device), indexing='ij')) * (1./resolution) + (0.5/resolution) | |
| 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) | |
| y_cam = uv[:, :, 1].view(N, -1) | |
| z_cam = torch.ones((N, M), device=cam2world_matrix.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) | |
| 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) | |
| ray_origins = cam_locs_world.unsqueeze(1).repeat(1, ray_dirs.shape[1], 1) | |
| return ray_origins, ray_dirs | |
| class RaySampler_zxc(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, cam_K, resolution, normalize=True): | |
| """ | |
| Create batches of rays and return origins and directions. | |
| cam2world_matrix: (N, 4, 4) | |
| intrinsics: (N, 3, 3) | |
| resolution: int | |
| ray_origins: (N, M, 3) | |
| ray_dirs: (N, M, 2) | |
| """ | |
| N, M = cam2world_matrix.shape[0], resolution**2 | |
| intrinsics = cam_K.clone() | |
| intrinsics[:, :2] *= resolution | |
| ray_origins, ray_dirs = [], [] | |
| for n in range(N): | |
| # K_inv = torch.from_numpy(np.linalg.inv(K)).to(c2w.device) | |
| K_inv = torch.linalg.inv(intrinsics[n]) | |
| c2w = cam2world_matrix[n] | |
| i, j = torch.meshgrid(torch.linspace(0, resolution - 1, resolution, device=intrinsics.device), | |
| torch.linspace(0, resolution - 1, resolution, device=intrinsics.device)) # pytorch's meshgrid has indexing='ij' | |
| i = i.t() | |
| j = j.t() | |
| homo_indices = torch.stack((i, j, torch.ones_like(i)), -1) # [H, W, 3] | |
| dirs = (K_inv[None, ...] @ homo_indices[..., None])[:, :, :, 0] | |
| # Rotate ray directions from camera frame to the world frame | |
| rays_d = (c2w[None, :3, :3] @ dirs[..., None])[:, :, :, 0] # [H, W, 3] | |
| if normalize: | |
| rays_d = torch.nn.functional.normalize(rays_d, dim=-1) | |
| # Translate camera frame's origin to the world frame. It is the origin of all rays. | |
| rays_o = c2w[:3, -1] | |
| rays_o = rays_o.expand(rays_d.shape) | |
| ray_dirs.append(rays_d.reshape(-1, 3)) | |
| ray_origins.append(rays_o.reshape(-1, 3)) | |
| return torch.stack(ray_origins, dim=0), torch.stack(ray_dirs, dim=0) |