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TRI-ML / dd3d

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.
MIT License
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Interesting #30

Open xmyqsh opened 2 years ago

xmyqsh commented 2 years ago

Big progress, good architecture. The inference latency should also be reduced.

But I have two questions. What's the density of 3d points outputted by the feature map? [Should as dense as image resolution, It doesn't matter.] As we know, image is more dense compared to point cloud produced by lidar, even dense lidar. [Alright, we can sub-sample it latter.] OK, state of the art 3d detector head can be used, such as center-point, it is efficient.

Hum, I think the only latency bottleneck may be the dense 3d point sub-sample part. 3d points sub-sample methods designed for Lidar generated data is in-efficient here, should be the bottleneck. The output of the confidence could be used here. But, is the predicted confidence confidence? Hope to see someone help to design good sub-sample method here.

Right, I have not read the code. Just a glimps of the paper. Lots of self guess.

xmyqsh commented 2 years ago

Wait a minute, just 3d point predicted in the feature map? Then how to get the 3d feature map? It is also time-consuming.

  1. efficient sub-sample method
  2. How to generate 3d feature map efficiently? or predict the 3d feature map directly?

I think these are two challenging point.