Source code for

# 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.
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"""Data parser for ScanNet dataset"""
import math
from dataclasses import dataclass, field
from pathlib import Path
from typing import Literal, Type

import cv2
import numpy as np
import torch

from nerfstudio.cameras import camera_utils
from nerfstudio.cameras.cameras import Cameras, CameraType
from import DataParser, DataParserConfig, DataparserOutputs
from import SceneBox

[docs]@dataclass class ScanNetDataParserConfig(DataParserConfig): """ScanNet dataset config. ScanNet dataset ( is a large-scale 3D dataset of indoor scenes. This dataparser assumes that the dense stream was extracted from .sens files. Expected structure of scene directory: .. code-block:: text root/ ├── color/ ├── depth/ ├── intrinsic/ ├── pose/ """ _target: Type = field(default_factory=lambda: ScanNet) """target class to instantiate""" data: Path = Path("data/scannet/scene0423_02") """Path to ScanNet folder with densely extracted scenes.""" scale_factor: float = 1.0 """How much to scale the camera origins by.""" scene_scale: float = 1.0 """How much to scale the region of interest by.""" center_method: Literal["poses", "focus", "none"] = "poses" """The method to use to center the poses.""" auto_scale_poses: bool = True """Whether to automatically scale the poses to fit in +/- 1 bounding box.""" train_split_fraction: float = 0.9 """The fraction of images to use for training. The remaining images are for eval.""" depth_unit_scale_factor: float = 1e-3 """Scales the depth values to meters. Default value is 0.001 for a millimeter to meter conversion."""
[docs]@dataclass class ScanNet(DataParser): """ScanNet DatasetParser""" config: ScanNetDataParserConfig def _generate_dataparser_outputs(self, split="train"): image_dir = / "color" depth_dir = / "depth" pose_dir = / "pose" img_dir_sorted = list(sorted(image_dir.iterdir(), key=lambda x: int(".")[0]))) depth_dir_sorted = list(sorted(depth_dir.iterdir(), key=lambda x: int(".")[0]))) pose_dir_sorted = list(sorted(pose_dir.iterdir(), key=lambda x: int(".")[0]))) first_img = cv2.imread(str(img_dir_sorted[0].absolute())) # type: ignore h, w, _ = first_img.shape image_filenames, depth_filenames, intrinsics, poses = [], [], [], [] K = np.loadtxt( / "intrinsic" / "intrinsic_color.txt") for img, depth, pose in zip(img_dir_sorted, depth_dir_sorted, pose_dir_sorted): pose = np.loadtxt(pose) pose = np.array(pose).reshape(4, 4) pose[:3, 1] *= -1 pose[:3, 2] *= -1 pose = torch.from_numpy(pose).float() # We cannot accept files directly, as some of the poses are invalid if np.isinf(pose).any(): continue poses.append(pose) intrinsics.append(K) image_filenames.append(img) depth_filenames.append(depth) # filter image_filenames and poses based on train/eval split percentage num_images = len(image_filenames) num_train_images = math.ceil(num_images * self.config.train_split_fraction) num_eval_images = num_images - num_train_images i_all = np.arange(num_images) i_train = np.linspace( 0, num_images - 1, num_train_images, dtype=int ) # equally spaced training images starting and ending at 0 and num_images-1 i_eval = np.setdiff1d(i_all, i_train) # eval images are the remaining images assert len(i_eval) == num_eval_images if split == "train": indices = i_train elif split in ["val", "test"]: indices = i_eval else: raise ValueError(f"Unknown dataparser split {split}") poses = torch.from_numpy(np.stack(poses).astype(np.float32)) intrinsics = torch.from_numpy(np.stack(intrinsics).astype(np.float32)) poses, transform_matrix = camera_utils.auto_orient_and_center_poses( poses, method="none", center_method=self.config.center_method, ) # Scale poses scale_factor = 1.0 if self.config.auto_scale_poses: scale_factor /= float(torch.max(torch.abs(poses[:, :3, 3]))) scale_factor *= self.config.scale_factor poses[:, :3, 3] *= scale_factor # Choose image_filenames and poses based on split, but after auto orient and scaling the poses. image_filenames = [image_filenames[i] for i in indices] depth_filenames = [depth_filenames[i] for i in indices] if len(depth_filenames) > 0 else [] intrinsics = intrinsics[indices.tolist()] poses = poses[indices.tolist()] # in x,y,z order # assumes that the scene is centered at the origin aabb_scale = self.config.scene_scale scene_box = SceneBox( aabb=torch.tensor( [[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32 ) ) cameras = Cameras( fx=intrinsics[:, 0, 0], fy=intrinsics[:, 1, 1], cx=intrinsics[:, 0, 2], cy=intrinsics[:, 1, 2], height=h, width=w, camera_to_worlds=poses[:, :3, :4], camera_type=CameraType.PERSPECTIVE, ) dataparser_outputs = DataparserOutputs( image_filenames=image_filenames, cameras=cameras, scene_box=scene_box, dataparser_scale=scale_factor, dataparser_transform=transform_matrix, metadata={ "depth_filenames": depth_filenames if len(depth_filenames) > 0 else None, "depth_unit_scale_factor": self.config.depth_unit_scale_factor, }, ) return dataparser_outputs