LHMPP

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LHMPP / core /models /rendering /utils /ray_marcher.py

Lingteng Qiu (邱陵腾）

rm assets & wheels

434b0b0 8 months ago

3.13 kB

# 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 Zexin He in 2023-2024.
# 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


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, bg_colors=None):
        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)

        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
        )
        weights = alpha * torch.cumprod(alpha_shifted, -2)[:, :, :-1]

        composite_rgb = torch.sum(weights * colors_mid, -2)
        weight_total = weights.sum(2)
        composite_depth = torch.sum(weights * depths_mid, -2) / weight_total

        # 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", False):
            composite_rgb = composite_rgb + 1 - weight_total
        else:
            assert (
                bg_colors is not None
            ), "Must provide bg_colors if white_back is False"
            composite_rgb = composite_rgb + bg_colors.unsqueeze(-1) * (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, bg_colors=None):
        composite_rgb, composite_depth, weights = self.run_forward(
            colors, densities, depths, rendering_options, bg_colors=bg_colors
        )

        return composite_rgb, composite_depth, weights

	# 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 Zexin He in 2023-2024.
	# 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


	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, bg_colors=None):
	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)

	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
	)
	weights = alpha * torch.cumprod(alpha_shifted, -2)[:, :, :-1]

	composite_rgb = torch.sum(weights * colors_mid, -2)
	weight_total = weights.sum(2)
	composite_depth = torch.sum(weights * depths_mid, -2) / weight_total

	# 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", False):
	composite_rgb = composite_rgb + 1 - weight_total
	else:
	assert (
	bg_colors is not None
	), "Must provide bg_colors if white_back is False"
	composite_rgb = composite_rgb + bg_colors.unsqueeze(-1) * (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, bg_colors=None):
	composite_rgb, composite_depth, weights = self.run_forward(
	colors, densities, depths, rendering_options, bg_colors=bg_colors
	)

	return composite_rgb, composite_depth, weights