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CalibNet

[DEPRECATED] This repository is no longer actively supported.

While the authors work on an update, please check out this unofficial implementation: CalibNet_pytorch 🔥 🙂

Code for our paper: CalibNet: Self-Supervised Extrinsic Calibration using 3D Spatial Transformer Networks

Check out our project page!

CalibNet_gif1

Prerequisites

CalibNet is trained on Tensorflow 1.3, CUDA 8.0, CUDNN 7.0.1

Installation

The code for point cloud distance loss is modified from PU-NET, PointNet++, PointSetGeneration.

This repository, thus, is based on Tensorflow and the TF operators from PointNet++ and PU-NET.

For installing tensorflow, please follow the official instructions in here. The code is tested under TF1.3 and Python 2.7 on Ubuntu 16.04.

For compiling TF operators, please check tf_xxx_compile.sh under each op subfolder in code/tf_ops folder, and change the path correctly to ../path/to/tensorflow/include. Note that you need to update nvcc, python and tensoflow include library if necessary. You also need to remove -D_GLIBCXX_USE_CXX11_ABI=0 flag in g++ command in order to compile correctly if necessary.

We are working to update the code and installation steps for the latest tensorflow versions.

Dataset Preparation

To prepare the dataset, run /dataset_files/dataset_builder_parallel.sh in the directory where you wish to store. We will also create a parser parsed_set.txt for the dataset, that contains the file names for training.

git clone https://github.com/epiception/CalibNet.git
or
svn checkout https://github.com/CalibNet/trunk/code (for the code)
cd ../path/to/dataset_directory
bash ../path/to/code_folder/dataset_files/dataset_builder_parallel.sh
cd ../path/to/CalibNet/code
python dataset_files/parser.py ../dataset_directory/2011_09_26/
Resnet-18

Pretrained Resnet-18 parameters can be found here.

Training

Before training, be sure to make requisite changes to the paths and training parameters in the config file config_res.py. We trained using 2 GPUs. The base code is written to support ops for the same device configuration.

To begin training:

CUDA_VISIBLE_DEVICES=<device_id1>,<device_id2> python -B train_model_combined.py
Trained Weights

Trained weights for the base variant (non-iterative) model is available here. This model was trained for 44 epochs. As mentioned in the paper, the iterative realignment model for better translation outputs will uploaded soon.

Evaluation/Test

Code for Direct Evaluation/Testing pipeline for point cloud calibration will be uploaded soon.