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304a759e85
Contract the scene to a sphere of radius 2. This is the same contraction used in MipNeRF 360. Add mipnerf 360 graph and config. It currently doesn't contain the other advancements made in the paper (sample proposal network, regularizers)
109 lines
3.1 KiB
Python
109 lines
3.1 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Dataset input structures.
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"""
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from dataclasses import dataclass
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from typing import List, Optional
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import torch
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from torchtyping import TensorType
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@dataclass
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class PointCloud:
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"""_summary_"""
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xyz: TensorType["num_points", 3] = None
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rgb: TensorType["num_points", 3] = None
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@dataclass
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class Semantics:
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"""_summary_"""
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stuff_classes: List[str] = None
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stuff_colors: List[List[int]] = None
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stuff_filenames: List[str] = None
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thing_classes: List[str] = None
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thing_colors: List[List[int]] = None
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thing_filenames: List[str] = None
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@dataclass
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class SceneBounds:
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"""Data to represent the scene bounds.
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aabb: axis-aligned bounding box
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aabb[0] is the minimum (x,y,z) point. aabb[1] is the maximum (x,y,z) point.
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"""
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aabb: TensorType[2, 3] = None
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def get_diagonal_length(self):
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"""Returns the longest diagonal length."""
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diff = self.aabb[1] - self.aabb[0]
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length = torch.sqrt((diff**2).sum() + 1e-20)
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return length
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def get_center(self):
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"""Returns the center of the box."""
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diff = self.aabb[1] - self.aabb[0]
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return self.aabb[0] + diff / 2.0
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def get_centered_and_scaled_scene_bounds(self, scale_factor=1.0):
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"""Returns a new box that has been shifted and rescaled to be centered
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about the origin."""
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return SceneBounds(aabb=(self.aabb - self.get_center()) * scale_factor)
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@dataclass
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class DatasetInputs:
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"""Dataset inputs for the image dataset and the ray generator.
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Args:
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image_filenames: Filenames for the images.
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...
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"""
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image_filenames: List[str]
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downscale_factor: int = 1
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intrinsics: torch.tensor = None
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camera_to_world: torch.tensor = None
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mask_filenames: List[str] = None
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depth_filenames: List[str] = None
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scene_bounds: SceneBounds = SceneBounds()
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semantics: Semantics = Semantics()
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point_cloud: PointCloud = PointCloud()
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alpha_color: Optional[TensorType[3]] = None
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def check_inputs(self):
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"""Check the inputs to make sure everything is okay."""
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assert self.intrinsics.dtype == torch.float32
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assert self.camera_to_world.dtype == torch.float32
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def save_to_folder_name(self, data_directory: str):
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"""Save the dataset inputs."""
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raise NotImplementedError
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def load_from_folder_name(self, data_directory: str):
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"""Load the saved dataset inputs."""
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raise NotImplementedError
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def as_dict(self) -> dict:
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"""Returns the dataclass as a dictionary."""
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return vars(self)
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