stereo_matching

This is a Tensorflow re-implementation of Luo, W., & Schwing, A. G. (n.d.). Efficient Deep Learning for Stereo Matching. (https://www.cs.toronto.edu/~urtasun/publications/luo_etal_cvpr16.pdf)

To run

Setup data folders

data
└───kitti_2015
    │─── training
         |───image_2
             |───000000_10.png
             |───000001_10.png
             |─── ...
         |───image_3
         |───disp_noc_0
         |─── ...
    │─── testing
         |───image_2
         |───image_3

Start training

python main.py --dataset kitti_2015 --patch-size 37 --disparity-range 201

Results

• After training for 40k iterations.
• Qualitative results on validation set.
• 3-pixel error evaluation on validation set.

KITTI 2015 Stereo

Example input images

Disparity Ground-truth

Example input patches

Qualitative results

Post-processing

• Cost-aggregation

Without cost-aggregation

With cost-aggregation

A closer look to observe the smoothing of predictions, without cost aggregation and with respectively:

Quantitative results

• To compare with results reported in paper, look at Table-5, column Ours(37).

  	3-pixel error (%)
  baseline (paper)	7.13
  baseline (re-implementation)	7.271
  baseline + CA (paper)	6.58
  baseline + CA (re-implementation)	6.527

KITTI 2012 Stereo

Qualitative results

Possible next steps

• Implement post processing to smoothen output.
• Look into error metrics and do quantitative analysis.
• Run inference on test video sequences.
• Instead of the batch matrix multiplication during inference, which constructs a B x H x W x W tensor, use a loop to compute cost volume over the disparity range. Tensorflow VM might figure out that it should parallelise operations over the loop.