5 papers
VGOcc: Learning Visual-Geometric Gaussians for Vision-Centric 3D Driving Occupancy Prediction
Junhong Lin, Xianda Guo, Kangli Wang +4
Vision-only occupancy prediction requires recovering a semantic 3D occupancy field from calibrated surround-view images, where each view provides observations with ambiguous depth…
AnyPcc: Compressing Any Point Cloud with a Single Universal Model
Kangli Wang, Qianxi Yi, Yuqi Ye +2
Generalization remains a critical challenge in deep learning-based point cloud geometry compression. While existing methods perform well on standard benchmarks, their performance c…
VGD: Visual Geometry Gaussian Splatting for Feed-Forward Surround-view Driving Reconstruction
Junhong Lin, Kangli Wang, Shunzhou Wang +3
Feed-forward surround-view autonomous driving scene reconstruction offers fast, generalizable inference ability, which faces the core challenge of ensuring generalization while ele…
A Novel Benchmark and Dataset for Efficient 3D Gaussian Splatting with Gaussian Point Cloud Compression
Kangli Wang, Shihao Li, Qianxi Yi +1
Recently, immersive media and autonomous driving applications have significantly advanced through 3D Gaussian Splatting (3DGS), which offers high-fidelity rendering and computation…
UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach
Kangli Wang, Wei Gao
Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity,…