DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes
Authors: Jia-Wei Liu, Yan-Pei Cao, Weijia Mao, Wenqiao Zhang, David Junhao Zhang, Jussi Keppo, Ying Shan, Xiaohu Qie, Mike Zheng Shou
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate De VRF on both synthetic and real-world dynamic scenes with different types of deformation. Experiments demonstrate that De VRF achieves two orders of magnitude speedup (100 faster) with on-par high-fidelity results compared to the previous state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | Jia-Wei Liu1 , Yan-Pei Cao2, Weijia Mao1, Wenqiao Zhang4, David Junhao Zhang1, Jussi Keppo5,6, Ying Shan2, Xiaohu Qie3, Mike Zheng Shou1 1 Show Lab, National University of Singapore 2 ARC Lab, 3 Tencent PCG 4 National University of Singapore 5 Business School, National University of Singapore 6 Institute of Operations Research and Analytics, National University of Singapore |
| Pseudocode | No | The paper describes its methods through text and mathematical equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and dataset are released in https://github.com/showlab/De VRF. |
| Open Datasets | Yes | The code and dataset are released in https://github.com/showlab/De VRF. |
| Dataset Splits | No | For each scene, we use 100-view static images and 4-view dynamic sequences with 50 frames (i.e., time steps) as training data for all approaches, and randomly select another 2 views at each time step for test. |
| Hardware Specification | Yes | We run all experiments on a single NVIDIA Ge Force RTX3090 GPU. |
| Software Dependencies | No | The paper mentions using a pre-trained RAFT model but does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | During training, we set ωRender = 1, ωCycle = 100, ωFlow = 0.005, and ωTV = 1 for all scenes. |