Super-LiDAR
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Implementation of "Deep Convolutional Compressed Sensing for LiDAR Depth Completion"
This project implements the ADNN architecture described in "Deep Convolutional Compressed Sensing for LiDAR Depth Completion" (http://arxiv.org/abs/1803.08949)
- Setup
** Dependencies
- Tensorflow 1.4
- Numpy 2.0
- PIL ** Data This project uses Tensorflow's binary tfrecord file to speed up training. Perform the following steps to set up the datasets for training and testing
- Download the KITTI depth completion dataset from http://www.cvlibs.net/datasets/kitti/eval_depth_all.php
- Unzip the various archives using the directions provided in the downloads
- Change the final line of kitti_depth_to_tfrecord.py to reflect the locations of your data and the desired location for the tfrecords, then run the file.
- Change lines 38, 43, 49, and 54 of main.py to reflect these locations as well.
- Training In order to train the three layer model described in the paper, create an output directory and run the command #+BEGIN_SRC bash python3 main.py