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Rethinking Pseudo-LiDAR Representation
The recently proposed pseudo-LiDAR based 3D detectors greatly improve the
benchmark of monocular/stereo 3D detection task. However, the underlying
mechanism remains obscure to the research community. In this paper, we perform
an in-depth investigation and observe that the efficacy of pseudo-LiDAR
representation comes from the coordinate transformation, instead of data
representation itself. Based on this observation, we design an image based CNN
detector named Patch-Net, which is more generalized and can be instantiated as
pseudo-LiDAR based 3D detectors. Moreover, the pseudo-LiDAR data in our
PatchNet is organized as the image representation, which means existing 2D CNN
designs can be easily utilized for extracting deep features from input data and
boosting 3D detection performance. We conduct extensive experiments on the
challenging KITTI dataset, where the proposed PatchNet outperforms all existing
pseudo-LiDAR based counterparts. Code has been made available at:
https://github.com/xinzhuma/patchnet.Comment: ECCV2020. Supplemental Material attache
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