1 citations · 1 across the 6 of their papers we have counts for
6 papers
Hi-LOAM: Hierarchical Implicit Neural Fields for LiDAR Odometry and Mapping
Zhiliu Yang, Jianyuan Zhang, Lianhui Zhao +2
LiDAR Odometry and Mapping (LOAM) is a pivotal technique for embodied-AI applications such as autonomous driving and robot navigation. Most existing LOAM frameworks are either cont…
SPORTS: Simultaneous Panoptic Odometry, Rendering, Tracking and Segmentation for Urban Scenes Understanding
Zhiliu Yang, Jinyu Dai, Jianyuan Zhang +1
The scene perception, understanding, and simulation are fundamental techniques for embodied-AI agents, while existing solutions are still prone to segmentation deficiency, dynamic…
GaRField++: Reinforced Gaussian Radiance Fields for Large-Scale 3D Scene Reconstruction
Hanyue Zhang, Zhiliu Yang, Xinhe Zuo +3
This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the scalability and accuracy challenges faced b…
Moving Object Proposals with Deep Learned Optical Flow for Video Object Segmentation
Ge Shi, Zhili Yang
Dynamic scene understanding is one of the most conspicuous field of interest among computer vision community. In order to enhance dynamic scene understanding, pixel-wise segmentati…
LISNeRF Mapping: LiDAR-based Implicit Mapping via Semantic Neural Fields for Large-Scale 3D Scenes
Jianyuan Zhang, Zhiliu Yang, Meng Zhang
Large-scale semantic mapping is crucial for outdoor autonomous agents to fulfill high-level tasks such as planning and navigation. This paper proposes a novel method for large-scal…
TivNe-SLAM: Dynamic Mapping and Tracking via Time-Varying Neural Radiance Fields
Chengyao Duan, Zhiliu Yang
Previous attempts to integrate Neural Radiance Fields (NeRF) into the Simultaneous Localization and Mapping (SLAM) framework either rely on the assumption of static scenes or requi…