3 papers
cs.GR2026
SAND: Spatially Adaptive Network Depth for Fast Sampling of Neural Implicit Surfaces
Chuanxiang Yang, Junhui Hou, Yuan Liu +5
Implicit neural representations are powerful for geometric modeling, but their practical use is often limited by the high computational cost of network evaluations. We observe that…
cs.LG2025
T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representation
Chuanxiang Yang, Yuanfeng Zhou, Guangshun Wei +4
Level-of-detail (LoD) representation is critical for efficiently modeling and transmitting various types of signals, such as images and 3D shapes. In this work, we propose a novel…
cs.CG2025
NeCGS: Neural Compression for 3D Geometry Sets
Siyu Ren, Junhui Hou, Weiyao Lin +1
We present NeCGS, the first neural compression paradigm, which can compress a geometry set encompassing thousands of detailed and diverse 3D mesh models by up to 900 times with hig…