7 papers
ProSGNeRF: Progressive Dynamic Neural Scene Graph with Frequency Modulated Foundation Model in Urban Scenes
Tianchen Deng, Yanbo Wang, Yejia Liu +5
Implicit neural representation has demonstrated promising results in 3D reconstruction on various scenes. However, existing approaches either struggle to model fast-moving objects…
Graph-Loc: Robust Graph-Based LiDAR Pose Tracking with Compact Structural Map Priors under Low Observability and Occlusion
Wentao Zhao, Yihe Niu, Zikun Chen +4
Map-based LiDAR pose tracking is essential for long-term autonomous operation, where onboard map priors need be compact for scalable storage and fast retrieval, while online observ…
Guided Diffusion-based Generation of Adversarial Objects for Real-World Monocular Depth Estimation Attacks
Yongtao Chen, Yanbo Wang, Wentao Zhao +3
Monocular Depth Estimation (MDE) serves as a core perception module in autonomous driving systems, but it remains highly susceptible to adversarial attacks. Errors in depth estimat…
MUT3R: Motion-aware Updating Transformer for Dynamic 3D Reconstruction
Guole Shen, Tianchen Deng, Xingrui Qin +6
Recent stateful recurrent neural networks have achieved remarkable progress on static 3D reconstruction but remain vulnerable to motion-induced artifacts, where non-rigid regions c…
GRS-SLAM3R: Real-Time Dense SLAM with Gated Recurrent State
Guole Shen, Tianchen Deng, Yanbo Wang +4
DUSt3R-based end-to-end scene reconstruction has recently shown promising results in dense visual SLAM. However, most existing methods only use image pairs to estimate pointmaps, o…
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…