README.md

metadata

license: cc-by-4.0

CoherentGS-DL3DV-Blur Dataset

Motivation 💡

To rigorously assess the generalization capability of CoherentGS in complex, unconstrained outdoor environments, we establish a new benchmark named DL3DV-Blur. This benchmark is derived from five diverse scenes within the DL3DV-10K dataset.

Citation Reference: Ling et al. (2024). DL3DV-10K: A Large-scale Dataset for Deep Learning-based 3D Vision.
https://arxiv.org/abs/2312.16256

Dataset Source 🔗

This dataset is constructed from select scenes of the official DL3DV-10K repository.

• DL3DV-10K GitHub: https://github.com/DL3DV-10K/Dataset

Data Format 📂

The dataset structure adheres to standard 3D vision dataset formats, where each scene (e.g., 0001) contains sub-folders for different view configurations (e.g., 3views, 6views, 9views).

Structure Overview

The hierarchical structure of the data is as follows:

dl3dv/
├── 0641-0720/
│   ├── 0001/                  # Scene ID 0001
│   │   ├── .work/
│   │   ├── 3views/            # 3-View Sub-set
│   │   │   ├── images/            # Raw input image files 
│   │   │   ├── ref_image/         # Reference Image
│   │   │   ├── sparse/            # Sparse reconstruction results (e.g., COLMAP output)
│   │   │   ├── cameras.json       # Camera parameter file
│   │   │   ├── ext_metadata.json  # Additional metadata
│   │   │   ├── hold=7             # Test set configuration
│   │   │   ├── intrinsics.json    # Camera intrinsics
│   │   │   ├── poses_bounds.npy   # Camera poses and scene bounds
│   │   │   ├── train_test_split_3.json # Train/Test split definition
│   │   │   └── transforms.json    # Coordinate transformation info
│   │   ├── 6views/            # 6-View Sub-set
│   │   └── 9views/            # 9-View Sub-set
│   ├── 0002/
│   ├── 0003/
│   ├── 0004/
│   └── 0005/
└── ...