8 citations · 34 across the 15 of their papers we have counts for
4 papers · 1 filter
DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction
Kaichen Zhou, Lanqing Hong, Changhao Chen +4
Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames. However, they neither fully…
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…
UnrealNAS: Can We Search Neural Architectures with Unreal Data?
Zhen Dong, Kaicheng Zhou, Guohao Li +5
Neural architecture search (NAS) has shown great success in the automatic design of deep neural networks (DNNs). However, the best way to use data to search network architectures i…
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…