5 papers · 1 filter
Incremental Joint Learning of Depth, Pose and Implicit Scene Representation on Monocular Camera in Large-scale Scenes
Tianchen Deng, Nailin Wang, Chongdi Wang +5
Dense scene reconstruction for photo-realistic view synthesis has various applications, such as VR/AR, autonomous vehicles. However, most existing methods have difficulties in larg…
SALT: A Flexible Semi-Automatic Labeling Tool for General LiDAR Point Clouds with Cross-Scene Adaptability and 4D Consistency
Yanbo Wang, Yongtao Chen, Chuan Cao +4
We propose a flexible Semi-Automatic Labeling Tool (SALT) for general LiDAR point clouds with cross-scene adaptability and 4D consistency. Unlike recent approaches that rely on cam…
PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustment
Tianchen Deng, Guole Shen, Tong Qin +5
Neural implicit scene representations have recently shown encouraging results in dense visual SLAM. However, existing methods produce low-quality scene reconstruction and low-accur…
SN-LiDAR: Semantic Neural Fields for Novel Space-time View LiDAR Synthesis
Yi Chen, Tianchen Deng, Wentao Zhao +4
Recent research has begun exploring novel view synthesis (NVS) for LiDAR point clouds, aiming to generate realistic LiDAR scans from unseen viewpoints. However, most existing appro…
SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds
Yanbo Wang, Wentao Zhao, Chuan Cao +3
Although LiDAR semantic segmentation advances rapidly, state-of-the-art methods often incorporate specifically designed inductive bias derived from benchmarks originating from mech…