3 papers
cs.CV2026
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…
cs.CV2026
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…
cs.CV2026
SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation
Yun Wang, Zhengjie Yang, Jiahao Zheng +3
Recent self-supervised stereo matching methods have made significant progress. They typically rely on the photometric consistency assumption, which presumes corresponding points ac…