Spaces:
Runtime error
Runtime error
| # 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 marcher takes the raw output of the implicit representation and uses the volume rendering equation to produce composited colors and depths. | |
| Based off of the implementation in MipNeRF (this one doesn't do any cone tracing though!) | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class MipRayMarcher2(nn.Module): | |
| def __init__(self, activation_factory): | |
| super().__init__() | |
| self.activation_factory = activation_factory | |
| def run_forward(self, colors, densities, depths, rendering_options, normals=None): | |
| dtype = colors.dtype | |
| deltas = depths[:, :, 1:] - depths[:, :, :-1] | |
| colors_mid = (colors[:, :, :-1] + colors[:, :, 1:]) / 2 | |
| densities_mid = (densities[:, :, :-1] + densities[:, :, 1:]) / 2 | |
| depths_mid = (depths[:, :, :-1] + depths[:, :, 1:]) / 2 | |
| # using factory mode for better usability | |
| densities_mid = self.activation_factory(rendering_options)(densities_mid).to(dtype) | |
| density_delta = densities_mid * deltas | |
| alpha = 1 - torch.exp(-density_delta).to(dtype) | |
| alpha_shifted = torch.cat([torch.ones_like(alpha[:, :, :1]), 1-alpha + 1e-10], -2) | |
| weights = alpha * torch.cumprod(alpha_shifted, -2)[:, :, :-1] | |
| weights = weights.to(dtype) | |
| composite_rgb = torch.sum(weights * colors_mid, -2) | |
| weight_total = weights.sum(2) | |
| # composite_depth = torch.sum(weights * depths_mid, -2) / weight_total | |
| composite_depth = torch.sum(weights * depths_mid, -2) | |
| # clip the composite to min/max range of depths | |
| composite_depth = torch.nan_to_num(composite_depth, float('inf')).to(dtype) | |
| composite_depth = torch.clamp(composite_depth, torch.min(depths), torch.max(depths)) | |
| if rendering_options.get('white_back', False): | |
| composite_rgb = composite_rgb + 1 - weight_total | |
| # rendered value scale is 0-1, comment out original mipnerf scaling | |
| # composite_rgb = composite_rgb * 2 - 1 # Scale to (-1, 1) | |
| return composite_rgb, composite_depth, weights | |
| def forward(self, colors, densities, depths, rendering_options, normals=None): | |
| if normals is not None: | |
| composite_rgb, composite_depth, composite_normals, weights = self.run_forward(colors, densities, depths, rendering_options, normals) | |
| return composite_rgb, composite_depth, composite_normals, weights | |
| composite_rgb, composite_depth, weights = self.run_forward(colors, densities, depths, rendering_options) | |
| return composite_rgb, composite_depth, weights | |