From the 1 of 6 linked papers with an AI index.
6 papers
CASA-SDF: Curriculum-Aware Spatial Adaptation with Curvature-Guided Density for Neural Implicit Surface Reconstruction
Lei Yang, Weiqing Li, Zhiyong Su +1
The paper introduces CASA-SDF, a framework that uses curriculum-aware spatial adaptation and curvature-guided density transformation to improve neural implicit surface reconstructi…
UGD: An Unsupervised Geometric Distance for Evaluating Real-world Noisy Point Cloud Denoising
Zhiyong Su, Jincan Wu, Yonghui Liu +2
Point cloud denoising is a fundamental and crucial challenge in real-world point cloud applications. Existing quantitative evaluation metrics for point cloud denoising methods are…
UPDA: Unsupervised Progressive Domain Adaptation for No-Reference Point Cloud Quality Assessment
Bingxu Xie, Fang Zhou, Jincan Wu +3
While no-reference point cloud quality assessment (NR-PCQA) approaches have achieved significant progress over the past decade, their performance often degrades substantially when…
Open-world Point Cloud Semantic Segmentation: A Human-in-the-loop Framework
Peng Zhang, Songru Yang, Jinsheng Sun +2
Open-world point cloud semantic segmentation (OW-Seg) aims to predict point labels of both base and novel classes in real-world scenarios. However, existing methods rely on resourc…
No-reference geometry quality assessment for colorless point clouds via list-wise rank learning
Zheng Li, Bingxu Xie, Chao Chu +2
Geometry quality assessment (GQA) of colorless point clouds is crucial for evaluating the performance of emerging point cloud-based solutions (e.g., watermarking, compression, and…
The Worse The Better: Content-Aware Viewpoint Generation Network for Projection-related Point Cloud Quality Assessment
Zhiyong Su, Bingxu Xie, Zheng Li +2
Through experimental studies, however, we observed the instability of final predicted quality scores, which change significantly over different viewpoint settings. Inspired by the…