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#DeepI2P: Image-to-Point Cloud Registration via Deep Classification

Summary

Video

PyTorch implementation for our CVPR 2021 paper DeepI2P. DeepI2P solves the problem of cross modality registration, i.e, solve the relative rotation R and translation t between the camera and the lidar.

DeepI2P: Image-to-Point Cloud Registration via Deep Classification<br> Jiaxin Li ¹, Gim Hee Lee ² <br> ¹ByteDance, ²National University of Singapore

Method

The intuition is to perform the Inverse Camera Projection, as shown in the images below.

Repo Structure

• data: Generate and process datasets
• evaluation: Registration codes, include Inverse Camera Projection, ICP, PnP
  • frustum_reg: C++ codes of the Inverse Camera Projection, using Gauss-Newton Optimization. Installation method is shown below. It requires the Ceres Solver.

  python evaluation/frustum_reg/setup.py install
  

  • icp: codes for ICP (Iterative Closest Point)
  • registration_lsq.py: Python code for Inverse Camera Projection, which utilizes the per-point coarse classification prediction, and the frustum_reg solver.
  • registration_pnp.py: Python code for PnP solver utilizing the per-point fine classification prediction.
• kitti: Training codes for KITTI
• nuscenes: Training codes for nuscenes
• oxford: Training codes for Oxford Robotcar dataset
• models: Networks and layers
  • 'index_max_ext': This is a custom operation from SO-Net, which is the backbone of our network. Installation:

  python models/index_max_ext/setup.py install
  

  • networks_img.py: Network to process images. It is a resnet-like structure.
  • networks_pc.py: Network to process point clouds, it is from SO-Net
  • network_united.py: Network to fuse information between point clouds and images.

Dataset and Models

• Oxford train/test split and evaluation dataset
• Oxford coarse classification model
• Oxford coarse & fine classification model