6 papers
Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity
Zhang Chen, Shuai Wan, Yuezhe Zhang +3
The unstructured and irregular nature of points poses a significant challenge for accurate point cloud quality assessment (PCQA), particularly in establishing accurate perceptual f…
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…
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
Yifan Zhang, Junhui Hou, Siyu Ren +3
This paper introduces a novel self-supervised learning framework for enhancing 3D perception in autonomous driving scenes. Specifically, our approach, namely NCLR, focuses on 2D-3D…
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…
DDM: A Metric for Comparing 3D Shapes Using Directional Distance Fields
Siyu Ren, Junhui Hou, Xiaodong Chen +2
Qualifying the discrepancy between 3D geometric models, which could be represented with either point clouds or triangle meshes, is a pivotal issue with board applications. Existing…
RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation
Zhang Chen, Shuai Wan, Siyu Ren +3
One of the main challenges in point cloud compression (PCC) is how to evaluate the perceived distortion so that the codec can be optimized for perceptual quality. Current standard…