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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 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 | |
| from pdb import set_trace as st | |
| class MipRayMarcher2(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| def run_forward(self, colors, densities, depths, rendering_options): | |
| 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 | |
| if rendering_options['clamp_mode'] == 'softplus': | |
| densities_mid = F.softplus( | |
| densities_mid - | |
| 1) # activation bias of -1 makes things initialize better | |
| else: | |
| assert False, "MipRayMarcher only supports `clamp_mode`=`softplus`!" | |
| density_delta = densities_mid * deltas | |
| alpha = 1 - torch.exp(-density_delta) | |
| alpha_shifted = torch.cat( | |
| [torch.ones_like(alpha[:, :, :1]), 1 - alpha + 1e-10], -2) | |
| T = torch.cumprod(alpha_shifted, -2) # transmittance | |
| weights = alpha * T[:, :, :-1] | |
| visibility = T[:, :, | |
| -1] # bg lambda, https://github.com/Kai-46/nerfplusplus/blob/ebf2f3e75fd6c5dfc8c9d0b533800daaf17bd95f/ddp_model.py#L101 | |
| # st() | |
| 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) # shapenet white background, no need this. | |
| # clip the composite to min/max range of depths | |
| composite_depth = torch.nan_to_num(composite_depth, float('inf')) | |
| composite_depth = torch.clamp(composite_depth, torch.min(depths), | |
| torch.max(depths)) | |
| if rendering_options.get('white_back', True): | |
| composite_rgb = composite_rgb + 1 - weight_total | |
| composite_rgb = composite_rgb * 2 - 1 # Scale (0,1) to (-1, 1) | |
| return composite_rgb, composite_depth, visibility, weights | |
| def forward(self, colors, densities, depths, rendering_options): | |
| composite_rgb, composite_depth, visibility, weights = self.run_forward( | |
| colors, densities, depths, rendering_options) | |
| return composite_rgb, composite_depth, visibility, weights | |