Source code for pytorch3d.renderer.mesh.rasterizer

# 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 dataclasses import dataclass
from typing import NamedTuple, Optional, Tuple, Union

import torch
import torch.nn as nn

from .rasterize_meshes import rasterize_meshes


# Class to store the outputs of mesh rasterization
[docs]class Fragments(NamedTuple): pix_to_face: torch.Tensor zbuf: torch.Tensor bary_coords: torch.Tensor dists: torch.Tensor
[docs]@dataclass class RasterizationSettings: """ Class to store the mesh rasterization params with defaults Members: image_size: Either common height and width or (height, width), in pixels. blur_radius: Float distance in the range [0, 2] used to expand the face bounding boxes for rasterization. Setting blur radius results in blurred edges around the shape instead of a hard boundary. Set to 0 for no blur. faces_per_pixel: (int) Number of faces to keep track of per pixel. We return the nearest faces_per_pixel faces along the z-axis. bin_size: Size of bins to use for coarse-to-fine rasterization. Setting bin_size=0 uses naive rasterization; setting bin_size=None attempts to set it heuristically based on the shape of the input. This should not affect the output, but can affect the speed of the forward pass. max_faces_per_bin: Only applicable when using coarse-to-fine rasterization (bin_size != 0); this is the maximum number of faces allowed within each bin. This should not affect the output values, but can affect the memory usage in the forward pass. Setting max_faces_per_bin=None attempts to set with a heuristic. perspective_correct: Whether to apply perspective correction when computing barycentric coordinates for pixels. None (default) means make correction if the camera uses perspective. clip_barycentric_coords: Whether, after any perspective correction is applied but before the depth is calculated (e.g. for z clipping), to "correct" a location outside the face (i.e. with a negative barycentric coordinate) to a position on the edge of the face. None (default) means clip if blur_radius > 0, which is a condition under which such outside-face-points are likely. cull_backfaces: Whether to only rasterize mesh faces which are visible to the camera. This assumes that vertices of front-facing triangles are ordered in an anti-clockwise fashion, and triangles that face away from the camera are in a clockwise order relative to the current view direction. NOTE: This will only work if the mesh faces are consistently defined with counter-clockwise ordering when viewed from the outside. z_clip_value: if not None, then triangles will be clipped (and possibly subdivided into smaller triangles) such that z >= z_clip_value. This avoids camera projections that go to infinity as z->0. Default is None as clipping affects rasterization speed and should only be turned on if explicitly needed. See clip.py for all the extra computation that is required. cull_to_frustum: Whether to cull triangles outside the view frustum. Culling involves removing all faces which fall outside view frustum. Default is False for performance as often not needed. """ image_size: Union[int, Tuple[int, int]] = 256 blur_radius: float = 0.0 faces_per_pixel: int = 1 bin_size: Optional[int] = None max_faces_per_bin: Optional[int] = None perspective_correct: Optional[bool] = None clip_barycentric_coords: Optional[bool] = None cull_backfaces: bool = False z_clip_value: Optional[float] = None cull_to_frustum: bool = False
[docs]class MeshRasterizer(nn.Module): """ This class implements methods for rasterizing a batch of heterogeneous Meshes. """
[docs] def __init__(self, cameras=None, raster_settings=None) -> None: """ Args: cameras: A cameras object which has a `transform_points` method which returns the transformed points after applying the world-to-view and view-to-ndc transformations. raster_settings: the parameters for rasterization. This should be a named tuple. All these initial settings can be overridden by passing keyword arguments to the forward function. """ super().__init__() if raster_settings is None: raster_settings = RasterizationSettings() self.cameras = cameras self.raster_settings = raster_settings
[docs] def to(self, device): # Manually move to device cameras as it is not a subclass of nn.Module if self.cameras is not None: self.cameras = self.cameras.to(device) return self
[docs] def transform(self, meshes_world, **kwargs) -> torch.Tensor: """ Args: meshes_world: a Meshes object representing a batch of meshes with vertex coordinates in world space. Returns: meshes_proj: a Meshes object with the vertex positions projected in NDC space NOTE: keeping this as a separate function for readability but it could be moved into forward. """ cameras = kwargs.get("cameras", self.cameras) if cameras is None: msg = "Cameras must be specified either at initialization \ or in the forward pass of MeshRasterizer" raise ValueError(msg) n_cameras = len(cameras) if n_cameras != 1 and n_cameras != len(meshes_world): msg = "Wrong number (%r) of cameras for %r meshes" raise ValueError(msg % (n_cameras, len(meshes_world))) verts_world = meshes_world.verts_padded() # NOTE: Retaining view space z coordinate for now. # TODO: Revisit whether or not to transform z coordinate to [-1, 1] or # [0, 1] range. eps = kwargs.get("eps", None) verts_view = cameras.get_world_to_view_transform(**kwargs).transform_points( verts_world, eps=eps ) # view to NDC transform to_ndc_transform = cameras.get_ndc_camera_transform(**kwargs) projection_transform = cameras.get_projection_transform(**kwargs).compose( to_ndc_transform ) verts_ndc = projection_transform.transform_points(verts_view, eps=eps) verts_ndc[..., 2] = verts_view[..., 2] meshes_ndc = meshes_world.update_padded(new_verts_padded=verts_ndc) return meshes_ndc
[docs] def forward(self, meshes_world, **kwargs) -> Fragments: """ Args: meshes_world: a Meshes object representing a batch of meshes with coordinates in world space. Returns: Fragments: Rasterization outputs as a named tuple. """ meshes_proj = self.transform(meshes_world, **kwargs) raster_settings = kwargs.get("raster_settings", self.raster_settings) # By default, turn on clip_barycentric_coords if blur_radius > 0. # When blur_radius > 0, a face can be matched to a pixel that is outside the # face, resulting in negative barycentric coordinates. clip_barycentric_coords = raster_settings.clip_barycentric_coords if clip_barycentric_coords is None: clip_barycentric_coords = raster_settings.blur_radius > 0.0 # If not specified, infer perspective_correct and z_clip_value from the camera cameras = kwargs.get("cameras", self.cameras) if raster_settings.perspective_correct is not None: perspective_correct = raster_settings.perspective_correct else: perspective_correct = cameras.is_perspective() if raster_settings.z_clip_value is not None: z_clip = raster_settings.z_clip_value else: znear = cameras.get_znear() if isinstance(znear, torch.Tensor): znear = znear.min().item() z_clip = None if not perspective_correct or znear is None else znear / 2 pix_to_face, zbuf, bary_coords, dists = rasterize_meshes( meshes_proj, image_size=raster_settings.image_size, blur_radius=raster_settings.blur_radius, faces_per_pixel=raster_settings.faces_per_pixel, bin_size=raster_settings.bin_size, max_faces_per_bin=raster_settings.max_faces_per_bin, clip_barycentric_coords=clip_barycentric_coords, perspective_correct=perspective_correct, cull_backfaces=raster_settings.cull_backfaces, z_clip_value=z_clip, cull_to_frustum=raster_settings.cull_to_frustum, ) return Fragments( pix_to_face=pix_to_face, zbuf=zbuf, bary_coords=bary_coords, dists=dists )