5 papers
Towards Practical Compression of 3D Gaussian Splatting
Pengpeng Yu, Yueru Chen, Fei Song +4
3D Gaussian Splatting (3DGS) enables high-quality novel-view synthesis but requires substantial storage. Existing compression methods often rely on spatial context modeling over ir…
CodecSplat: Ultra-Compact Latent Coding for Feed-Forward 3D Gaussian Splatting
Pengpeng Yu, Runqing Jiang, Qi Zhang +3
While feed-forward 3D Gaussian splatting reconstructs renderable Gaussian primitives from sparse context views without per-scene optimization, existing pipelines do not provide a c…
D3S2: Diffusion-Guided Dataset Distillation for Semantic Segmentation
Wenjie Zheng, Haoji Hu, Jiali Lu +2
Dataset distillation (DD) aims to compress large-scale datasets into compact synthetic sets while preserving training efficacy. However, existing studies mainly focus on image clas…
Towards Practical Lossless Neural Compression for LiDAR Point Clouds
Pengpeng Yu, Haoran Li, Runqing Jiang +4
LiDAR point clouds are fundamental to various applications, yet the extreme sparsity of high-precision geometric details hinders efficient context modeling, thereby limiting the co…
Re-Densification Meets Cross-Scale Propagation: Real-Time Neural Compression of LiDAR Point Clouds
Pengpeng Yu, Haoran Li, Runqing Jiang +3
LiDAR point clouds are fundamental to various applications, yet high-precision scans incur substantial storage and transmission overhead. Existing methods typically convert unorder…