Source code for pytorch3d.renderer.blending

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.


from typing import NamedTuple, Sequence, Union

import torch
from pytorch3d import _C


# Example functions for blending the top K colors per pixel using the outputs
# from rasterization.
# NOTE: All blending function should return an RGBA image per batch element


[docs]class BlendParams(NamedTuple): """ Data class to store blending params with defaults Members: sigma (float): Controls the width of the sigmoid function used to calculate the 2D distance based probability. Determines the sharpness of the edges of the shape. Higher => faces have less defined edges. gamma (float): Controls the scaling of the exponential function used to set the opacity of the color. Higher => faces are more transparent. background_color: RGB values for the background color as a tuple or as a tensor of three floats. """ sigma: float = 1e-4 gamma: float = 1e-4 background_color: Union[torch.Tensor, Sequence[float]] = (1.0, 1.0, 1.0)
[docs]def hard_rgb_blend( colors: torch.Tensor, fragments, blend_params: BlendParams ) -> torch.Tensor: """ Naive blending of top K faces to return an RGBA image - **RGB** - choose color of the closest point i.e. K=0 - **A** - 1.0 Args: colors: (N, H, W, K, 3) RGB color for each of the top K faces per pixel. fragments: the outputs of rasterization. From this we use - pix_to_face: LongTensor of shape (N, H, W, K) specifying the indices of the faces (in the packed representation) which overlap each pixel in the image. This is used to determine the output shape. blend_params: BlendParams instance that contains a background_color field specifying the color for the background Returns: RGBA pixel_colors: (N, H, W, 4) """ N, H, W, K = fragments.pix_to_face.shape device = fragments.pix_to_face.device # Mask for the background. is_background = fragments.pix_to_face[..., 0] < 0 # (N, H, W) background_color_ = blend_params.background_color if isinstance(background_color_, torch.Tensor): background_color = background_color_.to(device) else: background_color = colors.new_tensor(background_color_) # Find out how much background_color needs to be expanded to be used for masked_scatter. num_background_pixels = is_background.sum() # Set background color. pixel_colors = colors[..., 0, :].masked_scatter( is_background[..., None], background_color[None, :].expand(num_background_pixels, -1), ) # (N, H, W, 3) # Concat with the alpha channel. alpha = (~is_background).type_as(pixel_colors)[..., None] return torch.cat([pixel_colors, alpha], dim=-1) # (N, H, W, 4)
# Wrapper for the C++/CUDA Implementation of sigmoid alpha blend. class _SigmoidAlphaBlend(torch.autograd.Function): @staticmethod def forward(ctx, dists, pix_to_face, sigma): alphas = _C.sigmoid_alpha_blend(dists, pix_to_face, sigma) ctx.save_for_backward(dists, pix_to_face, alphas) ctx.sigma = sigma return alphas @staticmethod def backward(ctx, grad_alphas): dists, pix_to_face, alphas = ctx.saved_tensors sigma = ctx.sigma grad_dists = _C.sigmoid_alpha_blend_backward( grad_alphas, alphas, dists, pix_to_face, sigma ) return grad_dists, None, None # pyre-fixme[16]: `_SigmoidAlphaBlend` has no attribute `apply`. _sigmoid_alpha = _SigmoidAlphaBlend.apply
[docs]def sigmoid_alpha_blend(colors, fragments, blend_params: BlendParams) -> torch.Tensor: """ Silhouette blending to return an RGBA image - **RGB** - choose color of the closest point. - **A** - blend based on the 2D distance based probability map [1]. Args: colors: (N, H, W, K, 3) RGB color for each of the top K faces per pixel. fragments: the outputs of rasterization. From this we use - pix_to_face: LongTensor of shape (N, H, W, K) specifying the indices of the faces (in the packed representation) which overlap each pixel in the image. - dists: FloatTensor of shape (N, H, W, K) specifying the 2D euclidean distance from the center of each pixel to each of the top K overlapping faces. Returns: RGBA pixel_colors: (N, H, W, 4) [1] Liu et al, 'Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning', ICCV 2019 """ N, H, W, K = fragments.pix_to_face.shape pixel_colors = torch.ones((N, H, W, 4), dtype=colors.dtype, device=colors.device) pixel_colors[..., :3] = colors[..., 0, :] alpha = _sigmoid_alpha(fragments.dists, fragments.pix_to_face, blend_params.sigma) pixel_colors[..., 3] = alpha return pixel_colors
[docs]def softmax_rgb_blend( colors: torch.Tensor, fragments, blend_params: BlendParams, znear: Union[float, torch.Tensor] = 1.0, zfar: Union[float, torch.Tensor] = 100, ) -> torch.Tensor: """ RGB and alpha channel blending to return an RGBA image based on the method proposed in [1] - **RGB** - blend the colors based on the 2D distance based probability map and relative z distances. - **A** - blend based on the 2D distance based probability map. Args: colors: (N, H, W, K, 3) RGB color for each of the top K faces per pixel. fragments: namedtuple with outputs of rasterization. We use properties - pix_to_face: LongTensor of shape (N, H, W, K) specifying the indices of the faces (in the packed representation) which overlap each pixel in the image. - dists: FloatTensor of shape (N, H, W, K) specifying the 2D euclidean distance from the center of each pixel to each of the top K overlapping faces. - zbuf: FloatTensor of shape (N, H, W, K) specifying the interpolated depth from each pixel to to each of the top K overlapping faces. blend_params: instance of BlendParams dataclass containing properties - sigma: float, parameter which controls the width of the sigmoid function used to calculate the 2D distance based probability. Sigma controls the sharpness of the edges of the shape. - gamma: float, parameter which controls the scaling of the exponential function used to control the opacity of the color. - background_color: (3) element list/tuple/torch.Tensor specifying the RGB values for the background color. znear: float, near clipping plane in the z direction zfar: float, far clipping plane in the z direction Returns: RGBA pixel_colors: (N, H, W, 4) [0] Shichen Liu et al, 'Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning' """ N, H, W, K = fragments.pix_to_face.shape device = fragments.pix_to_face.device pixel_colors = torch.ones((N, H, W, 4), dtype=colors.dtype, device=colors.device) background_ = blend_params.background_color if not isinstance(background_, torch.Tensor): background = torch.tensor(background_, dtype=torch.float32, device=device) else: background = background_.to(device) # Weight for background color eps = 1e-10 # Mask for padded pixels. mask = fragments.pix_to_face >= 0 # Sigmoid probability map based on the distance of the pixel to the face. prob_map = torch.sigmoid(-fragments.dists / blend_params.sigma) * mask # The cumulative product ensures that alpha will be 0.0 if at least 1 # face fully covers the pixel as for that face, prob will be 1.0. # This results in a multiplication by 0.0 because of the (1.0 - prob) # term. Therefore 1.0 - alpha will be 1.0. alpha = torch.prod((1.0 - prob_map), dim=-1) # Weights for each face. Adjust the exponential by the max z to prevent # overflow. zbuf shape (N, H, W, K), find max over K. # TODO: there may still be some instability in the exponent calculation. # Reshape to be compatible with (N, H, W, K) values in fragments if torch.is_tensor(zfar): # pyre-fixme[16] zfar = zfar[:, None, None, None] if torch.is_tensor(znear): # pyre-fixme[16]: Item `float` of `Union[float, Tensor]` has no attribute # `__getitem__`. znear = znear[:, None, None, None] z_inv = (zfar - fragments.zbuf) / (zfar - znear) * mask z_inv_max = torch.max(z_inv, dim=-1).values[..., None].clamp(min=eps) weights_num = prob_map * torch.exp((z_inv - z_inv_max) / blend_params.gamma) # Also apply exp normalize trick for the background color weight. # Clamp to ensure delta is never 0. # pyre-fixme[6]: Expected `Tensor` for 1st param but got `float`. delta = torch.exp((eps - z_inv_max) / blend_params.gamma).clamp(min=eps) # Normalize weights. # weights_num shape: (N, H, W, K). Sum over K and divide through by the sum. denom = weights_num.sum(dim=-1)[..., None] + delta # Sum: weights * textures + background color weighted_colors = (weights_num[..., None] * colors).sum(dim=-2) weighted_background = delta * background pixel_colors[..., :3] = (weighted_colors + weighted_background) / denom pixel_colors[..., 3] = 1.0 - alpha return pixel_colors