3d object detection from point cloud
DOI:
https://doi.org/10.5564/jimdt.v6i1.3609Keywords:
computer vision, deep learning, object detection, point cloudAbstract
Here we present a comparison of two different deep learning architectures’ effectiveness along with two distinct detection head approaches for detecting point cloud ball objects. Two backbones that are explored are: VoxelNet, suited for organized point clouds, and PointNet, which handles unorganized point clouds. We modified and implemented SSD and Faster R-CNN detection heads for both backbones. It turns out that the PointNet backbone integrated with a customized Faster R-CNN detection head achieved higher accuracy compared to other combination of models.
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