| --- |
| 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} |
| } |
| ``` |
