This repository provides an implementation for the following paper.
Paper title: In Defense of Classical Image Processing: Fast Depth Completion on the CPU
the paper have employed classical image processing methods to predict depth in LIDAR images. To do so, the algorithm contains a set of MORPHOLOGY tricks.
Numpy and OpenCV is used to implement the approach.
Paper Abstract:
With the rise of data driven deep neural networks as a realization of universal function approximators, most research on computer vision problems has moved away from hand crafted classical image processing algorithms. This paper shows that with a well designed algorithm, we are capable of outperforming neural network based methods on the task of depth completion. The proposed algorithm is simple and fast, runs on the CPU, and relies only on basic image processing operations to perform depth completion of sparse LIDAR depth data. We evaluate our algorithm on the challenging KITTI depth completion benchmark, and at the time of submission, our method ranks f irst on the KITTI test server among all published methods. Furthermore, our algorithm is data independent, requiring no training data to perform the task at hand.
- Paper : arXiv page
- Main repository : Github
- My Presentation File : Powerpoint
- My Presentation File : PDF
- Clone the repository with following command :
git clone https://github.com/sabadijou/FastDepthCompletionCPU.git
cd FastDepthCompletionCPU
pip3 install -r requirements.txt
- Download dataset from below :
http://www.cvlibs.net/download.php?file=data_depth_selection.zip
- Extract downloaded file and copy contents of "Kitti\depth\depth_selection\val_selection_cropped " directory to the "dataset\kitti_validation_cropped"
- Run main.py