Add dataset card for SparseCam4D

README.md

@@ -1,3 +1,71 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- image-to-3d
+---
+
+# SparseCam4D: Spatio-Temporally Consistent 4D Reconstruction from Sparse Cameras
+
+This repository contains the demo dataset for **SparseCam4D**, a framework for high-quality 4D reconstruction from sparse and uncalibrated camera inputs.
+
+[**Project page**](https://inspatio.github.io/sparse-cam4d/) | [**Paper**](https://arxiv.org/abs/2603.26481) | [**GitHub**](https://github.com/inspatio/sparse-cam4d)
+
+## Data Layout
+
+The expected data layout for the dataset is as follows:
+
+```
+balloon1/
+├── depth/
+│   ├── cam01/              # per-frame depth maps for training camera cam01 (*.npy)
+│   ├── cam06/              # per-frame depth maps for training camera cam06 (*.npy)
+│   ├── cam10/              # per-frame depth maps for training camera cam10 (*.npy)
+│   ├── cam01.mp4           # depth video visualization
+│   ├── cam06.mp4
+│   └── cam10.mp4
+├── images/                 # all input images, named as <cam>_<time>.png
+├── preprocess/
+│   ├── time_0000/
+│   │   ├── diffusion/      # pseudo-view images generated by ViewCrafter at t=0
+│   │   └── sparse/0/       # COLMAP sparse reconstruction at t=0 (cameras.bin, points3D.ply, ...)
+│   ├── time_0001/
+│   │   └── diffusion/      # pseudo-view images at t=1
+│   └── ...                 # time_0002 ~ time_0099, each with diffusion/
+├── sfm_transforms_extend.json   # camera intrinsics + extrinsics for all views and timestamps
+├── vc_roma_sfm_300.ply          # initial point cloud (SfM + RoMa dense matching)
+├── transforms_train.json        # camera poses for training split
+└── transforms_test.json         # camera poses for test split
+```
+
+**Depth maps** are estimated by [Video Depth Anything](https://github.com/DepthAnything/Video-Depth-Anything) on the training-camera videos.
+
+**Pseudo-view images** under `preprocess/time_*/diffusion/` are synthesized by [ViewCrafter](https://github.com/Drexubery/ViewCrafter) from training cameras to cover additional viewpoints at each timestamp with sparse camera poses estimated by [VGGT](https://github.com/facebookresearch/vggt).
+
+## Sample Usage
+
+### Training
+
+To train the model on this dataset, edit the `source_path` and `model_path` fields in the config file, then run:
+
+```shell
+python train.py --config configs/nvidia/balloon1.yaml
+```
+
+### Rendering and Evaluation
+
+After training and performing pose alignment, you can render and evaluate using:
+
+```shell
+python render.py --config configs/nvidia/balloon1.yaml --skip_train --iteration 30000
+```
+
+## Citation
+
+```bibtex
+@article{pan2026sparsecam4d,
+  title={SparseCam4D: Spatio-Temporally Consistent 4D Reconstruction from Sparse Cameras},
+  author={Pan, Weihong and Zhang, Xiaoyu and Zhang, Zhuang and Ye, Zhichao and Wang, Nan and Liu, Haomin and Zhang, Guofeng},
+  journal={arXiv preprint arXiv:2603.26481},
+  year={2026}
+}
+```