4 papers
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…
TopoLiDM: Topology-Aware LiDAR Diffusion Models for Interpretable and Realistic LiDAR Point Cloud Generation
Jiuming Liu, Zheng Huang, Mengmeng Liu +4
LiDAR scene generation is critical for mitigating real-world LiDAR data collection costs and enhancing the robustness of downstream perception tasks in autonomous driving. However,…