252 citations · 3.3k across the 92 of their papers we have counts for
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DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
Tao Ma, Xuemeng Yang, Hongbin Zhou +9
Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D…
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion
Xin Li, Tao Ma, Yuenan Hou +8
LiDAR-camera fusion methods have shown impressive performance in 3D object detection. Recent advanced multi-modal methods mainly perform global fusion, where image features and poi…
Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentation
Zheng Tong, Tao Ma, Ju Huyan +1
Pavement damage segmentation has benefited enormously from deep learning. % and large-scale datasets. However, few current public datasets limit the potential exploration of deep l…
Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis
Ben Fei, Weidong Yang, Wenming Chen +5
Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress…
Variable-Viewpoint Representations for 3D Object Recognition
Tengyu Ma, Joel Michelson, James Ainooson +3
For the problem of 3D object recognition, researchers using deep learning methods have developed several very different input representations, including "multi-view" snapshots take…
The Toybox Dataset of Egocentric Visual Object Transformations
Xiaohan Wang, Tengyu Ma, James Ainooson +4
In object recognition research, many commonly used datasets (e.g., ImageNet and similar) contain relatively sparse distributions of object instances and views, e.g., one might see…