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Stress-Testing Point Cloud Registration on Automotive LiDAR

Our ML4AD @ NeurIPS 2022 paper: Stress-Testing Point Cloud Registration on Automotive LiDAR / Amnon Drory, Raja Giryes, Shai Avidan

To train and test rigid point-cloud registration (PCR) algorithms with automotive LiDAR datasets, one must select a subset of point-cloud pairs. Previously, simple heuristics were used to create such registration sets, e.g., using a spacing of 10 meters. We provide a smart algorithm that selects challenging sets, that include a balanced sampling from the various situations that appear in a dataset (various offsets, rotations, etc.). Screenshot from 2022-11-14 08-21-43

We provide registration sets that were produced by our algorithm, for the Apollo-Southbay and NuScenes datasets. In our paper, we use these sets to train and benchmark some recent and popular registration algorithms. All algorithms use FCGF deep features, and differ in their method for robustly estimating the 6-DOF motion. Surprisingly, we find that the fastest and most accurate results come not from recent algorithms such as Teaser++ and PointDSC, but rather from a modern version of RANSAC.

time_and_recall_comparison_B_to_B_tight

This project contains our registration sets, code for creating such sets, and the code we used for benchmarking various PCR algorithms.

Contents of Directories

  1. BalancedDatasetGenerator: Code for creating registration sets

  2. balanced_sets: Contains the balanced sets used in the paper (as lists of frame indices and relative motions).

  3. Experiments: Code used to run most of the experiments shown in the paper. Also used to train the PointDSC network. based on PointDSC code

  4. FCGF_FAST: Our code for training an FCGF network. Also includes code for the refinement experiment.

  5. DGR: Somewhat modified code for training a DGR network. Based on DGR code

  6. GC-RANSAC: Some additions to GC-RANSAC code, including Edge-Length Constraints.

Requirements

The code for creating registration sets has quite minimal requirements. Reproducing our experiments, however, requires a variety of code packages, leading unfortunately to incompatibilities and complications. In the Requirements directory we provided as a reference .yml files that detail two environments we used: one for experiments involving the GC-RANSAC code base, and one for other experiments. To recreate these environments, we recommend running:

conda env create -f Requirements/basis.yml

And then installing the minkowski engine, which is needed for FCGF and DGR. Additional code packages, such as GC-RANSAC and TEASER++, might not be able to coexist and will need to be installed each in a separate environment.

Datasets

Our balanced registration sets are selected from Apollo-Southbay and NuScenes. To run our code, one must have these datasets available locally. They can be downloaded from:

  1. Apollo-Southbay
  2. NuScenes (Full dataset, v1.0, Trainval and Test)

The following files in our code need to be adjusted to reflect the paths to these datasets:

Experiments/dataloader/paths.py
BalancedDatasetGenerator/datasets/paths.py
FCGF_FR/dataloader/paths.py
DGR/dataloader/paths.py

Example Command Lines

cd Experiments 
python -m test --fcgf_weights_file ../weights/ApolloSouthbay.400.pth --dataset A --algo RANSAC --mode GPF --iters 50000 # Testing RANSAC+GPF on Apollo-Southbay-Balanced
python -m test --fcgf_weights_file ../weights/ApolloSouthbay.400.pth --dataset B --algo RANSAC --mode MMN --iters 1000000 --GC_conf 0.9995 # Testing RANSAC with mutual-nearest neighbors filtering (MNN) cross-domain, on NuScenes-Boston-Balanced
python -m test --fcgf_weights_file ../weights/ApolloSouthbay.400.pth --dataset A --algo TEASER # Testing with TEASER++
python -m train --fcgf_weights_file ../weights/ApolloSouthbay.400.pth --dataset A # training PointDSC network on Apollo-Southbay-Balanced
python -m test --chosen_snapshot PointDSC_ApolloSouthbay_??????? --fcgf_weights_file ../weights/ApolloSouthbay.400.pth --dataset S --algo PointDSC # Testing PointDSC cross-domain, on NuScenes-Singapore-Balanced (replace question marks with actual output from previous command)
cd ..

cd FCGF_FAST
python -m train B # training FCGF network for dataset NuScenes-Boston-Balanced
./refinement_parallel.sh B # perform refinement test
cd ..

cd BalancedDatasetGenerator
python -m GenerateBalancedSet.py