# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""
Dataset input structures.
"""
from dataclasses import dataclass
from typing import Tuple, Union
import torch
import viser.transforms as vtf
from jaxtyping import Float
from torch import Tensor
[docs]@dataclass
class SceneBox:
"""Data to represent the scene box."""
aabb: Float[Tensor, "2 3"]
"""aabb: axis-aligned bounding box.
aabb[0] is the minimum (x,y,z) point.
aabb[1] is the maximum (x,y,z) point."""
[docs] def within(self, pts: Float[Tensor, "n 3"]):
"""Returns a boolean mask indicating whether each point is within the box."""
return torch.all(pts > self.aabb[0], dim=-1) & torch.all(pts < self.aabb[1], dim=-1)
[docs] def get_diagonal_length(self):
"""Returns the longest diagonal length."""
diff = self.aabb[1] - self.aabb[0]
length = torch.sqrt((diff**2).sum() + 1e-20)
return length
[docs] def get_center(self):
"""Returns the center of the box."""
diff = self.aabb[1] - self.aabb[0]
return self.aabb[0] + diff / 2.0
[docs] def get_centered_and_scaled_scene_box(self, scale_factor: Union[float, torch.Tensor] = 1.0):
"""Returns a new box that has been shifted and rescaled to be centered
about the origin.
Args:
scale_factor: How much to scale the camera origins by.
"""
return SceneBox(aabb=(self.aabb - self.get_center()) * scale_factor)
[docs] @staticmethod
def get_normalized_positions(positions: Float[Tensor, "*batch 3"], aabb: Float[Tensor, "2 3"]):
"""Return normalized positions in range [0, 1] based on the aabb axis-aligned bounding box.
Args:
positions: the xyz positions
aabb: the axis-aligned bounding box
"""
aabb_lengths = aabb[1] - aabb[0]
normalized_positions = (positions - aabb[0]) / aabb_lengths
return normalized_positions
[docs] @staticmethod
def from_camera_poses(poses: Float[Tensor, "*batch 3 4"], scale_factor: float) -> "SceneBox":
"""Returns the instance of SceneBox that fully envelopes a set of poses
Args:
poses: tensor of camera pose matrices
scale_factor: How much to scale the camera origins by.
"""
xyzs = poses[..., :3, -1]
aabb = torch.stack([torch.min(xyzs, dim=0)[0], torch.max(xyzs, dim=0)[0]])
return SceneBox(aabb=aabb * scale_factor)
[docs]@dataclass
class OrientedBox:
R: Float[Tensor, "3 3"]
"""R: rotation matrix."""
T: Float[Tensor, "3"]
"""T: translation vector."""
S: Float[Tensor, "3"]
"""S: scale vector."""
[docs] def within(self, pts: Float[Tensor, "n 3"]):
"""Returns a boolean mask indicating whether each point is within the box."""
R, T, S = self.R, self.T, self.S.to(pts)
H = torch.eye(4, device=pts.device, dtype=pts.dtype)
H[:3, :3] = R
H[:3, 3] = T
H_world2bbox = torch.inverse(H)
pts = torch.cat((pts, torch.ones_like(pts[..., :1])), dim=-1)
pts = torch.matmul(H_world2bbox, pts.T).T[..., :3]
comp_l = torch.tensor(-S / 2)
comp_m = torch.tensor(S / 2)
mask = torch.all(torch.concat([pts > comp_l, pts < comp_m], dim=-1), dim=-1)
return mask
[docs] @staticmethod
def from_params(
pos: Tuple[float, float, float], rpy: Tuple[float, float, float], scale: Tuple[float, float, float]
):
"""Construct a box from position, rotation, and scale parameters."""
R = torch.tensor(vtf.SO3.from_rpy_radians(rpy[0], rpy[1], rpy[2]).as_matrix())
T = torch.tensor(pos)
S = torch.tensor(scale)
return OrientedBox(R=R, T=T, S=S)