320 citations · 963 across the 78 of their papers we have counts for
10 papers · 1 filter
FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentation
Chang Won Lee, Selina Leveugle, Svetlana Stolpner +4
Anomaly segmentation is an essential capability for safety-critical robotics applications that must be aware of unexpected events. Normalizing flows (NFs), a class of generative mo…
Toward General Object-level Mapping from Sparse Views with 3D Diffusion Priors
Ziwei Liao, Binbin Xu, Steven L. Waslander
Object-level mapping builds a 3D map of objects in a scene with detailed shapes and poses from multi-view sensor observations. Conventional methods struggle to build complete shape…
SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
Yang Zhou, Hao Shao, Letian Wang +3
Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. However, the scarcity of…
A Photorealistic Dataset and Vision-Based Algorithm for Anomaly Detection During Proximity Operations in Lunar Orbit
Selina Leveugle, Chang Won Lee, Svetlana Stolpner +4
NASA's forthcoming Lunar Gateway space station, which will be uncrewed most of the time, will need to operate with an unprecedented level of autonomy. One key challenge is enabling…
Image-to-Lidar Relational Distillation for Autonomous Driving Data
Anas Mahmoud, Ali Harakeh, Steven Waslander
Pre-trained on extensive and diverse multi-modal datasets, 2D foundation models excel at addressing 2D tasks with little or no downstream supervision, owing to their robust represe…
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features
Letian Wang, Seung Wook Kim, Jiawei Yang +7
We propose DistillNeRF, a self-supervised learning framework addressing the challenge of understanding 3D environments from limited 2D observations in outdoor autonomous driving sc…