Hi, I am a research scientist at NVIDIA Research Taiwan. My main research interest is 3D scene understanding and interactions using pointclouds, 3D Gaussians, and digital humans.
Email / CV / Google Scholar / LinkedIn
I'm interested in computer vision for 3D scene understanding. Representative papers are highlighted.
arXiv, 2026
A framework for multi-view promptable segmentation that achieves 3D consistency using pointmaps—3D points reconstructed from unposed images by recent visual geometry models, such as VGGT, PI3, etc.
[Project] [Paper (TBU)] [Code (TBU)]
arXiv, 2026
Sparse voxel representation for 3D scene understanding.
arXiv, 2025
We strengthen voxel-wise associations among parent-child and sibling voxel groups, resulting in smoother surface reconstruction.
arXiv, 2025
An efficient rendering algorithm for 3D Gaussians that involve high-dimensional feature vectors. Our Q-render achieves ∼43.7× speed gains compared to recent studies when rendering 512-D feature maps.
[Project] [Paper] [Code (TBU)]
CVPR, 2025 (highlight Top 3.7%)
Distill language knowledge into 3D Gaussian Splatting. + Winner of Qualcomm Innovation Fellowship Korea
CVPR, 2025
Introduce a data generation pipeline that leverages 2D foundation models to synthesize 3D mask-text paired data.
CVPR, 2025
Extend the rasterization technique into the sparse voxel representation for the image rendering task.
ICCV, 2023
Apply spacetime surface regularization technique for 4D surface reconstruction and dynamic scene rendering.
[Paper]
ECCV, 2022
Design a new MLP-only architecture for 3D points.
ICLR, 2022
Perform the point cloud upsampling and denoising tasks simultaneously. Demonstrate good generalization performance.
[Paper]
ICCV, 2021
Propose deep learning based depth fusion algorithm for indoor scene reconstruction.
[Paper]
RA-L, 2021
Estimation dense depth maps by fusing LiDAR points and stereo images.
[Paper (RA-L)] [Paper (ICRA)] [Video]