MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation
Reiner Birkl Diana Wofk Matthias Müller
We release MiDaS v3.1 for monocular depth estimation, offering a variety of
new models based on different encoder backbones. This release is motivated by
the success of transformers in computer vision, with a large variety of
pretrained vision transformers now available. We explore how using the most
promising vision transformers as image encoders impacts depth estimation
quality and runtime of the MiDaS architecture. Our investigation also includes
recent convolutional approaches that achieve comparable quality to vision
transformers in image classification tasks. While the previous release MiDaS
v3.0 solely leverages the vanilla vision transformer ViT, MiDaS v3.1 offers
additional models based on BEiT, Swin, SwinV2, Next-ViT and LeViT. These models
offer different performance-runtime tradeoffs. The best model improves the
depth estimation quality by 28% while efficient models enable downstream tasks
requiring high frame rates. We also describe the general process for
integrating new backbones. A video summarizing the work can be found at
https://youtu.be/UjaeNNFf9sE and the code is available at
https://github.com/isl-org/MiDaS.