Datasets:

active-vision-lab
/

oxford-day-and-night

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README.md ADDED Viewed

	@@ -0,0 +1,147 @@

+---
+task_categories:
+- image-to-3d
+- image-feature-extraction
+language:
+- en
+tags:
+- NVS
+- 3DGS
+- Relocalization
+- Egocentric
+size_categories:
+- 100K<n<1M
+---
+# Oxford Day and Night Dataset
+## Overview
+We recorded 124 egocentric videos of 5 locations in Oxford, UK, under 3 different lightning conditions, day, dusk, and night.
+This dataset offers a unique combination of:
+* large-scale (30 kilometers camera trajectory and 40,000 square meters area.)
+* egocentric view
+* varying environments (outdoor + indoor)
+* varying lightning conditions (day, dusk, night)
+* accurate camera parameters (via a robust multi-session SLAM system provided by Meta [ARIA MPS](https://facebookresearch.github.io/projectaria_tools/docs/ARK/mps).)
+* ground truth 3D geometry (via state of the art LIDAR scanned 3D models)
+## Table of Contents
+- [Overview](#overview)
+- [Data Structure](#data-structure)
+  - [File explanation](#file-explanation)
+    - [Directory `mp4`](#directory-mp4)
+    - [Directory `mps`](#directory-mps)
+    - [Directory `ns_processed`](#directory-ns_processed)
+    - [Directory `visual_reloc`](#directory-visual_reloc)
+  - [Ground truth 3D geometry](#ground-truth-3d-geometry)
+- [Download](#download)
+- [Application: Novel View Synthesis](#application-novel-view-synthesis)
+- [Application: Visual Localization](#application-visual-localization)
+## Data Structure
+```
+[4.0K]  aria/bodleian-library
+├── [4.0K]  mp4
+│   └── [4.0K]  blur  [44 .mp4 files]
+├── [4.0K]  mps
+│   └── [4.0K]  multi
+│       ├── [ 17G]  day_23.tar.gz
+│       ├── [ 943]  day_23.txt
+│       ├── [ 23G]  day_night_44.tar.gz
+│       ├── [1.8K]  day_night_44.txt
+│       ├── [5.2G]  night_21.tar.gz
+│       └── [ 861]  night_21.txt
+├── [4.0K]  ns_processed
+│   └── [4.0K]  multi
+│       ├── [4.0K]  fisheye624
+│       │   ├── [4.3G]  day_23.tar.gz
+│       │   ├── [7.0G]  day_night_44.tar.gz
+│       │   └── [2.7G]  night_21.tar.gz
+│       ├── [4.0K]  undistorted_all_valid
+│       │   ├── [4.3G]  day_23.tar.gz
+│       │   ├── [6.9G]  day_night_44.tar.gz
+│       │   └── [2.6G]  night_21.tar.gz
+│       └── [4.0K]  undistorted_max_fov
+│           ├── [4.0G]  day_23.tar.gz
+│           ├── [6.1G]  day_night_44.tar.gz
+│           └── [2.1G]  night_21.tar.gz
+└── [4.0K]  visual_reloc
+    ├── [4.0K]  colmap
+    │   ├── [4.0K]  bin
+    │   │   ├── [  56]  cameras.bin
+    │   │   ├── [172M]  images.bin
+    │   │   └── [250M]  points3D.bin
+    │   └── [4.0K]  text
+    │       ├── [ 138]  cameras.txt
+    │       ├── [157M]  images.txt
+    │       └── [278M]  points3D.txt
+    └── [4.0K]  imagelists
+        ├── [ 85K]  db_imagelist.txt
+        ├── [192K]  db_imagelist_with_intrinsics.txt
+        ├── [ 44K]  query_day_imagelist.txt
+        ├── [ 99K]  query_day_imagelist_with_intrinsics.txt
+        ├── [ 99K]  query_night_imagelist.txt
+        └── [221K]  query_night_imagelist_with_intrinsics.txt
+```
+### File explanation:
+#### Directory `mp4`
+Contains faceblurred video. Mainly for dataset preview.
+#### Directory `mps`
+Contains multi-session MPS results, ie, semi-dense point clouds, per image camera parameters and 2D observations.
+#### Directory `ns_processed`
+For **NVS** tasks, contains processed images and poses in `ns_processed` directory.
+* poses: camera poses are provided in two convenient conventions:
+    * `transforms.json`: 4x4 `C2W` camera transformations in OpenGL coordinate. This format is directly used by NerfStudio.
+    * `transforms_opencv.json`: 4x4 `W2C` camera transformations in OpenCV coordinate. This format is widely used in 3DGS, SLAM, and SfM community.
+* images: we provide both fisheye and undistorted images.
+    * `fisheye624`: original fisheye RGB image recorded by ARIA glasses.
+    * `undistorted_max_fov`: undistorted using an approximate max fov given the lens parameters. These images are fully undistorted but has black borders. Comparing with `undistorted_all_valid`, it has larger field of view but you need to handle the black borders using valid pixel masks (provided in the `tar.gz` file).
+    * `undistorted_all_valid`: undistorted using an smaller fov comparing with `undistorted_max_fov`. All pixels on these images are valid (no black borders). These images offers easier usage but smaller field of view.
+* lighting: pattern `day_<num_sessions>`, `night_<num_sessions>`, `day_night_<num_sessions>` denotes sessions grouped by different lighting conditions, where `<num_sessions>` is the number of sessions in this group.
+#### Directory `visual_reloc`
+For **Visual Localization** tasks, this can be easily plugged into HLoc benchmark and Scene Coordinate Regression networks training.
+* `colmap`: contains poses, 2D observations, 3D points in COLMAP format (`.bin` and `.txt`).
+* `imagelists`: contains training and testing split.
+    * `db_*.txt`: image list when building the database for HLoc, and when training Scene Coordinate Regression networks.
+    * `query_*.txt`: image list for testing.
+### Ground truth 3D geometry
+We use 3D Laser scanned point clouds provide by [Oxford-Spires](https://dynamic.robots.ox.ac.uk/datasets/oxford-spires/) dataset. Download the dataset from:
+* HuggingFace [link](https://huggingface.co/datasets/ori-drs/oxford_spires_dataset).
+* Google Drive [link](https://dynamic.robots.ox.ac.uk/datasets/oxford-spires/#googledrive).
+## Download
+We provided a script to download the dataset `hf_download.py`.
+Usage:
+```bash
+pip install -U "huggingface_hub[cli]"
+python hf_download.py -o <your_local_dir>/oxford-day-and-night
+```
+or directly run the following python code.
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(
+    repo_id="active-vision-lab/oxford-day-and-night",
+    repo_type="dataset",
+    local_dir="<your_local_dir>/oxford-day-and-night"
+)
+```
+## Application: Novel View Synthesis
+This dataset can be easily trained using NerfStudio and 3DGS-extended codebases.
+Train a `splatfacto` or `splatfacto-w` model on a day-only split:
+```bash
+ns-train splatfacto --data <path>
+ns-train splatfacto-w-light --data <path>
+```
+For `splatfacto-w-light` and `splatfacto-w`, you need to install the `splatfacto-w` following the instructions in [link1](https://github.com/KevinXu02/splatfacto-w) or [link2](https://docs.nerf.studio/nerfology/methods/splatw.html).
+## Application: Visual Localization
+Popular visual localization systems provide good support for COLMAP-formatted data, for example, HLoc benchmark, ACE0, Mast3r and etc.
+We convert our dataset to COLMAP format (`.bin` and `.txt`) to facilitate the usage of data on existing visual localization systems.
+Our COLMAP-formatted data can be found in `aria/<scene_name>/visual_reloc/colmap`.
+Exploring Localization with this COLMAP-formatted data is straightforward. See more details in [HLoc](https://github.com/cvg/Hierarchical-Localization) benchmark.

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