🔥 News
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📢 Actively seeking PhD or Research Assistant positions for 2026!
I am looking for opportunities to pursue a PhD or work as a Research Assistant starting in 2026. Please feel free to reach out if you have relevant positions available.
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- 2025.01: Welcome to my personal website!
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Publication
View my papers and projects here.
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PointCNN++: Performant Convolution on Native Points
L. Li,
H. Zhong,
R. Bu,
M. Sun,
W. Chen,
B. Chen,
Y. Li
arXiv:2511.23227
paper
Existing convolutional learning methods for 3D point cloud data are divided into two paradigms: point-based methods that preserve geometric precision but often face performance challenges, and voxel-based methods that achieve high efficiency through quantization at the cost of geometric fidelity. We propose PointCNN++, a novel architectural design that fundamentally mitigates this precision-performance trade-off by generalizing sparse convolution from voxels to points, treating voxel-based convolution as a specialized, degraded case of our more general point-based convolution.
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Robust Single-shot Structured Light 3D Imaging via Neural Feature Decoding
J. Li,
Q. Dai,
L. Li,
et al.
SIGGRAPH Asia 2025
A learning-based structured light decoding framework utilizing neural feature embeddings for robust pixel correspondence, improving reconstruction accuracy in complex lighting and material conditions.
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