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pulsar interface unification.
Summary: This diff builds on top of the `pulsar integration` diff to provide a unified interface for the existing PyTorch3D point renderer and Pulsar. For more information about the pulsar backend, see the release notes and the paper (https://arxiv.org/abs/2004.07484). For information on how to use the backend, see the point cloud rendering notebook and the examples in the folder docs/examples. The unified interfaces are completely consistent. Switching the render backend is as easy as using `renderer = PulsarPointsRenderer(rasterizer=rasterizer).to(device)` instead of `renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor)` and adding the `gamma` parameter to the forward function. All PyTorch3D camera types are supported as far as possible; keyword arguments are properly forwarded to the camera. The `PerspectiveCamera` and `OrthographicCamera` require znear and zfar as additional parameters for the forward pass. Reviewed By: nikhilaravi Differential Revision: D21421443 fbshipit-source-id: 4aa0a83a419592d9a0bb5d62486a1cdea9d73ce6
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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from .external.kornia_angle_axis_to_rotation_matrix import (
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angle_axis_to_rotation_matrix as axis_angle_to_matrix,
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)
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from .rotation_conversions import (
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axis_angle_to_matrix,
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euler_angles_to_matrix,
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matrix_to_euler_angles,
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matrix_to_quaternion,
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1
pytorch3d/transforms/external/__init__.py
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1
pytorch3d/transforms/external/__init__.py
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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#!/usr/bin/env python3
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"""
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This file contains the great angle axis to rotation matrix conversion
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from kornia (https://github.com/arraiyopensource/kornia). The license
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can be found in kornia_license.txt.
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The method is used unchanged; the documentation has been adjusted
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to match our doc format.
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"""
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import torch
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def angle_axis_to_rotation_matrix(angle_axis):
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"""Convert 3d vector of axis-angle rotation to 4x4 rotation matrix
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Args:
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angle_axis (Tensor): tensor of 3d vector of axis-angle rotations.
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Returns:
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Tensor: tensor of 3x3 rotation matrix.
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Shape:
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- Input: :math:`(N, 3)`
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- Output: :math:`(N, 3, 3)`
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Example:
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..code-block::python
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>>> input = torch.rand(1, 3) # Nx3
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>>> output = tgm.angle_axis_to_rotation_matrix(input) # Nx3x3
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>>> output = tgm.angle_axis_to_rotation_matrix(input) # Nx3x3
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"""
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def _compute_rotation_matrix(angle_axis, theta2, eps=1e-6):
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# We want to be careful to only evaluate the square root if the
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# norm of the angle_axis vector is greater than zero. Otherwise
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# we get a division by zero.
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k_one = 1.0
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theta = torch.sqrt(theta2)
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wxyz = angle_axis / (theta + eps)
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wx, wy, wz = torch.chunk(wxyz, 3, dim=1)
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cos_theta = torch.cos(theta)
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sin_theta = torch.sin(theta)
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r00 = cos_theta + wx * wx * (k_one - cos_theta)
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r10 = wz * sin_theta + wx * wy * (k_one - cos_theta)
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r20 = -wy * sin_theta + wx * wz * (k_one - cos_theta)
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r01 = wx * wy * (k_one - cos_theta) - wz * sin_theta
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r11 = cos_theta + wy * wy * (k_one - cos_theta)
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r21 = wx * sin_theta + wy * wz * (k_one - cos_theta)
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r02 = wy * sin_theta + wx * wz * (k_one - cos_theta)
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r12 = -wx * sin_theta + wy * wz * (k_one - cos_theta)
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r22 = cos_theta + wz * wz * (k_one - cos_theta)
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rotation_matrix = torch.cat(
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[r00, r01, r02, r10, r11, r12, r20, r21, r22], dim=1
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)
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return rotation_matrix.view(-1, 3, 3)
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def _compute_rotation_matrix_taylor(angle_axis):
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rx, ry, rz = torch.chunk(angle_axis, 3, dim=1)
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k_one = torch.ones_like(rx)
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rotation_matrix = torch.cat(
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[k_one, -rz, ry, rz, k_one, -rx, -ry, rx, k_one], dim=1
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)
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return rotation_matrix.view(-1, 3, 3)
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# stolen from ceres/rotation.h
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_angle_axis = torch.unsqueeze(angle_axis + 1e-6, dim=1)
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# _angle_axis.register_hook(lambda grad: pdb.set_trace())
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# _angle_axis = 1e-6
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theta2 = torch.matmul(_angle_axis, _angle_axis.transpose(1, 2))
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theta2 = torch.squeeze(theta2, dim=1)
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# compute rotation matrices
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rotation_matrix_normal = _compute_rotation_matrix(angle_axis, theta2)
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rotation_matrix_taylor = _compute_rotation_matrix_taylor(angle_axis)
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# create mask to handle both cases
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eps = 1e-6
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mask = (theta2 > eps).view(-1, 1, 1).to(theta2.device)
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mask_pos = (mask).type_as(theta2)
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mask_neg = (mask == False).type_as(theta2) # noqa
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# create output pose matrix
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batch_size = angle_axis.shape[0]
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rotation_matrix = torch.eye(3).to(angle_axis.device).type_as(angle_axis)
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rotation_matrix = rotation_matrix.view(1, 3, 3).repeat(batch_size, 1, 1)
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# fill output matrix with masked values
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rotation_matrix[..., :3, :3] = (
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mask_pos * rotation_matrix_normal + mask_neg * rotation_matrix_taylor
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)
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return rotation_matrix.to(angle_axis.device).type_as(angle_axis) # Nx4x4
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201
pytorch3d/transforms/external/kornia_license.txt
vendored
201
pytorch3d/transforms/external/kornia_license.txt
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Apache License
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