metadata
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 | Paper | GitHub
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 on the training-camera videos.
Pseudo-view images under preprocess/time_*/diffusion/ are synthesized by ViewCrafter from training cameras to cover additional viewpoints at each timestamp with sparse camera poses estimated by 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:
python train.py --config configs/nvidia/balloon1.yaml
Rendering and Evaluation
After training and performing pose alignment, you can render and evaluate using:
python render.py --config configs/nvidia/balloon1.yaml --skip_train --iteration 30000
Citation
@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}
}