5 citations · 14 across the 7 of their papers we have counts for
7 papers
Amelia: A Large Dataset and Benchmark for Airport Surface Movement Forecasting
Ingrid Navarro, Pablo Ortega-Kral, Jay Patrikar +6
Demand for air travel is rising, straining existing aviation infrastructure. In the US, more than 90% of airport control towers are understaffed, falling short of FAA and union sta…
FoundLoc: Vision-based Onboard Aerial Localization in the Wild
Yao He, Ivan Cisneros, Nikhil Keetha +6
Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GN…
CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame
Yujing Lou, Zelin Ye, Yang You +5
Various recent methods attempt to implement rotation-invariant 3D deep learning by replacing the input coordinates of points with relative distances and angles. Due to the incomple…
Multimodal Object Detection via Probabilistic Ensembling
Yi-Ting Chen, Jinghao Shi, Zelin Ye +3
Object detection with multimodal inputs can improve many safety-critical systems such as autonomous vehicles (AVs). Motivated by AVs that operate in both day and night, we study mu…
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes
Yang You, Zelin Ye, Yujing Lou +5
3D object detection has attracted much attention thanks to the advances in sensors and deep learning methods for point clouds. Current state-of-the-art methods like VoteNet regress…
Transferable Active Grasping and Real Embodied Dataset
Xiangyu Chen, Zelin Ye, Jiankai Sun +4
Grasping in cluttered scenes is challenging for robot vision systems, as detection accuracy can be hindered by partial occlusion of objects. We adopt a reinforcement learning (RL)…
3D Objectness Estimation via Bottom-up Regret Grouping
Zelin Ye, Yan Hao, Liang Xu +2
3D objectness estimation, namely discovering semantic objects from 3D scene, is a challenging and significant task in 3D understanding. In this paper, we propose a 3D objectness me…