A randomized algorithm for quickly finding approximate nearest neighbor matches between image patches. More information on https://gfx.cs.princeton.edu/pubs/Barnes_2009_PAR/
The goal of the assignment is to implement the multi-view patch algorithm for depthmap generation.
To this end you shall download the datasets fountain-P11, Herz-Jesu-P8, entry-P10 from the Strecha MVS evaluation website https://icwww.epfl.ch/~marquez/multiview/denseMVS.html and use the provided camera pose and calibration information provided for each image. Use the Patchmatch sample and propagation scheme alternating among the four image directions (left-to-right, top-to-bottom, right-to-left, bottom to top) and report the progress after each propagation direction.
a) Select three images from each of the datasets/scenes and generate for each a depth map Show the resulting depth maps after each iteration. b) Report the accuracy of each generated depth map compared to the available ground truth, by
- Report the average pixel error for each of the depth map
- Generate an error map (an image where the magnitude of the estimation error is stored at the pixel position) using Matlab’s “jet” colormap for visualization
- Plot the cumulative error distribution for each depth map Notes:
- Start from a random depth initialization for each pixel in the depth map
- Use any window size photo-consistency measure you deem adequate. Justify your design choice Bonus: Use multiple photo-consistency measures and compare performance
Expects a folder named 'data' with the relevant scenes from the Strecha Dataset, in the same repository.