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BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection

July 2022

tl;dr: LSS with explicit depth supervision and Efficient Voxel Pooling.

Overall impression

In LSS, depth estimation is implicitly learnt without camera info. The accuracy of depth estimation is surprisingly inadequate (pred-gt curve shows very poor correlation). Replacing the depth with ground truth depth will lead to huge improvement, indicating that the quality of intermediate depth is the key to improving multi-view 3D object detection.

The idea is relatively simple, but the engineering effort was executed beautifully, with in-depth analysis.

Key ideas

  • Explicit depth supervision: The depth is supervised by projected point clouds, with a min poling and a one-hot encoding.
  • Depth correction is introduced to account for the error in extrinsics calibration (and online extrinsic disturbance). Instead of finding the exactly correct calibration, BEVDepth proposes to enlarge the receptive field to compensate for inaccurate lidar-camera alignment. In this way, the mis-aligned depth GT is able to attend to the feature at right position. --> This is a good point. May be very useful for early fusion.
  • Camera aware depth prediction --> This is an alternative to CamConv
    • Use MLP to scale up the dimension of intrinsics to higher dimension (e.g., 128d)
    • Do the same for extrinsics (R and t), and concatenate with intrinsics feature.
    • Then the camera calib feature is used to weight camera features with SENet (squeeze and excitation) module.

Technical details

  • Efficient Voxel Pooling --> See also in BEVFusion and M2BEV.
    • Assigning each grid in BEV to a thread, and each thread adds the associated point features in parallel.
    • This speeds up the Voxel Pooling in LSS. The voxel pooling operation alone is speed up x100. Overall pipeline speed by x3.
    • The cumsum trick used by LSS cannot run in parallel.
  • Multiframe
    • Ego motion compensation of the pseudo-lidar point cloud of the previous frame, then aggregated with the current frame.
    • Time window is only 2, same to BEVDet4d.

Notes