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Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving

August 2019

tl;dr: Concurrent proj with Pseudo-lidar but with color embedding. SOTA together with CMU PL for mono 3DOD as of 08/2019.

Overall impression

This paper seems to be inspired by pseudo-lidar but did not mention it explicitly. The paper's writing leaves much to be desired. Performance-wise, it is very similar to CMU's e2e pseudo-lidar paper.

Simply concat D to RGB does not help much, such as ROI 10D and MLF.

The paper proposed a simple segmentation method to combat the long-tail problem, although it does not clearly state so.

Key ideas

  • The paper has several tweaks of F-Pointnet.
    • First a much coarser thresholding is used to replace point cloud segmentation in the frustum. --> this is used to combat the long-tail problem, as also mentioned in CMU's end-to-end pseudo-lidar paper.
    • RGB info is used to enhance point cloud. However direct concat does not work. The paper proposed attention mechanism to fuse RGB with XYZ point info.
  • Depth prior baed point cloud segmentation: threshold by depth interval based on depth mean. It is much faster and much more robust (5 ms on CPU).

Technical details

  • The calib file is used to lift RGB to point cloud. However this can be learned for the same camera, if we only care about one set of camera.
  • The RGB fusion module is also effective for real point cloud based methods.

Notes

  • Looks like crude thresholding works for pseudo-point cloud segmentation --> Maybe use radar info directly correct segmented pseudo-point cloud?
  • Maybe we should relax evaluation method for distant objects? 1. 3d bbox gt is labeled on sparse point cloud, 2. Not as critical as nearby objects.