18 citations · 26 across the 7 of their papers we have counts for
7 papers
VIPeR: Visual Incremental Place Recognition with Adaptive Mining and Continual Learning
Yuhang Ming, Minyang Xu, Xingrui Yang +5
Visual place recognition (VPR) is an essential component of many autonomous and augmented/virtual reality systems. It enables the systems to robustly localize themselves in large-s…
Benchmarking Neural Radiance Fields for Autonomous Robots: An Overview
Yuhang Ming, Xingrui Yang, Weihan Wang +4
Neural Radiance Fields (NeRF) have emerged as a powerful paradigm for 3D scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstru…
Stereo-NEC: Enhancing Stereo Visual-Inertial SLAM Initialization with Normal Epipolar Constraints
Weihan Wang, Chieh Chou, Ganesh Sevagamoorthy +7
We propose an accurate and robust initialization approach for stereo visual-inertial SLAM systems. Unlike the current state-of-the-art method, which heavily relies on the accuracy…
PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields
Zheng Chen, Qingan Yan, Huangying Zhan +7
Identifying spatially complete planar primitives from visual data is a crucial task in computer vision. Prior methods are largely restricted to either 2D segment recovery or simpli…
EDI: ESKF-based Disjoint Initialization for Visual-Inertial SLAM Systems
Weihan Wang, Jiani Li, Yuhang Ming +1
Visual-inertial initialization can be classified into joint and disjoint approaches. Joint approaches tackle both the visual and the inertial parameters together by aligning observ…
Real-Time Dense 3D Mapping of Underwater Environments
Weihan Wang, Bharat Joshi, Nathaniel Burgdorfer +4
This paper addresses real-time dense 3D reconstruction for a resource-constrained Autonomous Underwater Vehicle (AUV). Underwater vision-guided operations are among the most challe…
Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation
Liyan Chen, Weihan Wang, Philippos Mordohai
We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the o…