Depth Evaluation

This benchmark is related to our work published in Sparsity Invariant CNNs (THREEDV 2017). It contains over 93 thousand depth maps with corresponding raw LiDaR scans and RGB images, aligned with the "raw data" of the KITTI dataset. Given the large amount of training data, this dataset shall allow a training of complex deep learning models for the tasks of depth completion and single image depth prediction. Also, we provide manually selected images with unpublished depth maps to serve as a benchmark for those two challenging tasks.

Make sure to unzip annotated depth maps and raw LiDaR scans into the same directory so that all corresponding files end up in the same folder structure. The structure of all provided depth maps is aligned with the structure of our raw data to easily find corresponding left and right images, or other provided information.

This is our depth completion evaluation, where sparse depth maps have to be completed to
full density (with or without RGB image guidance). It consists of 93k training and 1k eval as well
as 1k test images.
This is our single image depth prediction evaluation, where dense depth maps have to be
predicted from a single RGB image input. It consists of 93k training and 1k eval as well as
500 test images.

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