11 citations · 20 across the 10 of their papers we have counts for
7 papers · 1 filter
RoadVGGT: Road-Structure-Aware Feed-Forward Road Surface Reconstruction
Han Jiao, Chen Liu, Jiakai Sun +6
Large-scale road surface reconstruction supports high-definition mapping, autonomous-driving perception, annotation, and simulation. Existing road-specialized optimization methods…
RepText: Rendering Visual Text via Replicating
Haofan Wang, Yujia Xu, Yimeng Li +5
Although contemporary text-to-image generation models have achieved remarkable breakthroughs in producing visually appealing images, their capacity to generate precise and flexible…
Labeling Indoor Scenes with Fusion of Out-of-the-Box Perception Models
Yimeng Li, Navid Rajabi, Sulabh Shrestha +2
The image annotation stage is a critical and often the most time-consuming part required for training and evaluating object detection and semantic segmentation models. Deployment o…
Self-supervised Pre-training for Semantic Segmentation in an Indoor Scene
Sulabh Shrestha, Yimeng Li, Jana Kosecka
The ability to endow maps of indoor scenes with semantic information is an integral part of robotic agents which perform different tasks such as target driven navigation, object se…
Uncertainty Aware Proposal Segmentation for Unknown Object Detection
Yimeng Li, Jana Kosecka
Recent efforts in deploying Deep Neural Networks for object detection in real world applications, such as autonomous driving, assume that all relevant object classes have been obse…
Learning View and Target Invariant Visual Servoing for Navigation
Yimeng Li, Jana Kosecka
The advances in deep reinforcement learning recently revived interest in data-driven learning based approaches to navigation. In this paper we propose to learn viewpoint invariant…
Simultaneous Mapping and Target Driven Navigation
Georgios Georgakis, Yimeng Li, Jana Kosecka
This work presents a modular architecture for simultaneous mapping and target driven navigation in indoors environments. The semantic and appearance stored in 2.5D map is distilled…