224 citations · 708 across the 24 of their papers we have counts for
5 papers · 1 filter
Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR
Sangyun Shin, Stuart Golodetz, Madhu Vankadari +3
Deep learning has led to great progress in the detection of mobile (i.e. movement-capable) objects in urban driving scenes in recent years. Supervised approaches typically require…
Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds
Ta-Ying Cheng, Qingyong Hu, Qian Xie +2
Sampling is a key operation in point-cloud task and acts to increase computational efficiency and tractability by discarding redundant points. Universal sampling algorithms (e.g.,…
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces
Jia-Xing Zhong, Kaichen Zhou, Qingyong Hu +3
Scene flow is a powerful tool for capturing the motion field of 3D point clouds. However, it is difficult to directly apply flow-based models to dynamic point cloud classification…
Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV
Stuart Golodetz, Madhu Vankadari, Aluna Everitt +3
Unmanned aerial vehicles (UAVs) have been used for many applications in recent years, from urban search and rescue, to agricultural surveying, to autonomous underground mine explor…
SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds
Qingyong Hu, Bo Yang, Sheikh Khalid +3
With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to fa…