content: # 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
  