File size: 5,007 Bytes
87c126b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
# MIT License
# Copyright (c) 2022 Petr Kellnhofer
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import torch
from pdb import set_trace as st
def transform_vectors(matrix: torch.Tensor,
vectors4: torch.Tensor) -> torch.Tensor:
"""
Left-multiplies MxM @ NxM. Returns NxM.
"""
res = torch.matmul(vectors4, matrix.T)
return res
def normalize_vecs(vectors: torch.Tensor) -> torch.Tensor:
"""
Normalize vector lengths.
"""
return vectors / (torch.norm(vectors, dim=-1, keepdim=True))
def torch_dot(x: torch.Tensor, y: torch.Tensor):
"""
Dot product of two tensors.
"""
return (x * y).sum(-1)
def get_ray_limits_box(rays_o: torch.Tensor, rays_d: torch.Tensor,
box_side_length):
"""
Author: Petr Kellnhofer
Intersects rays with the [-1, 1] NDC volume.
Returns min and max distance of entry.
Returns -1 for no intersection.
https://www.scratchapixel.com/lessons/3d-basic-rendering/minimal-ray-tracer-rendering-simple-shapes/ray-box-intersection
"""
o_shape = rays_o.shape
rays_o = rays_o.detach().reshape(-1, 3)
rays_d = rays_d.detach().reshape(-1, 3)
bb_min = [
-1 * (box_side_length / 2), -1 * (box_side_length / 2),
-1 * (box_side_length / 2)
]
bb_max = [
1 * (box_side_length / 2), 1 * (box_side_length / 2),
1 * (box_side_length / 2)
]
bounds = torch.tensor([bb_min, bb_max],
dtype=rays_o.dtype,
device=rays_o.device)
is_valid = torch.ones(rays_o.shape[:-1], dtype=bool, device=rays_o.device)
# Precompute inverse for stability.
invdir = 1 / rays_d
sign = (invdir < 0).long()
# Intersect with YZ plane.
tmin = (bounds.index_select(0, sign[..., 0])[..., 0] -
rays_o[..., 0]) * invdir[..., 0]
tmax = (bounds.index_select(0, 1 - sign[..., 0])[..., 0] -
rays_o[..., 0]) * invdir[..., 0]
# Intersect with XZ plane.
tymin = (bounds.index_select(0, sign[..., 1])[..., 1] -
rays_o[..., 1]) * invdir[..., 1]
tymax = (bounds.index_select(0, 1 - sign[..., 1])[..., 1] -
rays_o[..., 1]) * invdir[..., 1]
# Resolve parallel rays.
is_valid[torch.logical_or(tmin > tymax, tymin > tmax)] = False
# Use the shortest intersection.
tmin = torch.max(tmin, tymin)
tmax = torch.min(tmax, tymax)
# Intersect with XY plane.
tzmin = (bounds.index_select(0, sign[..., 2])[..., 2] -
rays_o[..., 2]) * invdir[..., 2]
tzmax = (bounds.index_select(0, 1 - sign[..., 2])[..., 2] -
rays_o[..., 2]) * invdir[..., 2]
# Resolve parallel rays.
is_valid[torch.logical_or(tmin > tzmax, tzmin > tmax)] = False
# Use the shortest intersection.
tmin = torch.max(tmin, tzmin)
tmax = torch.min(tmax, tzmax)
# Mark invalid.
tmin[torch.logical_not(is_valid)] = -1
tmax[torch.logical_not(is_valid)] = -2
return tmin.reshape(*o_shape[:-1], 1), tmax.reshape(*o_shape[:-1], 1)
def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):
"""
Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.
Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.
"""
# create a tensor of 'num' steps from 0 to 1
steps = torch.arange(num, dtype=torch.float32,
device=start.device) / (num - 1)
# reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings
# - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript
# "cannot statically infer the expected size of a list in this contex", hence the code below
for i in range(start.ndim):
steps = steps.unsqueeze(-1)
# the output starts at 'start' and increments until 'stop' in each dimension
out = start[None] + steps * (stop - start)[None]
return out
|