16 citations · 42 across the 14 of their papers we have counts for
7 papers · 1 filter
Relative Energy Learning for LiDAR Out-of-Distribution Detection
Zizhao Li, Zhengkang Xiang, Jiayang Ao +2
Out-of-distribution (OOD) detection is a critical requirement for reliable autonomous driving, where safety depends on recognizing road obstacles and unexpected objects beyond the…
rareboost3d: a synthetic lidar dataset with enhanced rare classes
Shutong Lin, Zhengkang Xiang, Jianzhong Qi +1
Real-world point cloud datasets have made significant contributions to the development of LiDAR-based perception technologies, such as object segmentation for autonomous driving. H…
Mapping Hidden Heritage: Self-supervised Pre-training on High-Resolution LiDAR DEM Derivatives for Archaeological Stone Wall Detection
Zexian Huang, Mashnoon Islam, Brian Armstrong +3
Historic dry-stone walls hold significant cultural and environmental importance, serving as historical markers and contributing to ecosystem preservation and wildfire management du…
Look Beyond: Two-Stage Scene View Generation via Panorama and Video Diffusion
Xueyang Kang, Zhengkang Xiang, Zezheng Zhang +1
Novel view synthesis (NVS) from a single image is highly ill-posed due to large unobserved regions, especially for views that deviate significantly from the input. While existing m…
Surfel-based 3D Registration with Equivariant SE(3) Features
Xueyang Kang, Hang Zhao, Kourosh Khoshelham +1
Point cloud registration is crucial for ensuring 3D alignment consistency of multiple local point clouds in 3D reconstruction for remote sensing or digital heritage. While various…
Out-of-distribution detection in 3D applications: a review
Zizhao Li, Xueyang Kang, Joseph West +1
The ability to detect objects that are not prevalent in the training set is a critical capability in many 3D applications, including autonomous driving. Machine learning methods fo…
SG-LDM: Semantic-Guided LiDAR Generation via Latent-Aligned Diffusion
Zhengkang Xiang, Zizhao Li, Amir Khodabandeh +1
Lidar point cloud synthesis based on generative models offers a promising solution to augment deep learning pipelines, particularly when real-world data is scarce or lacks diversit…