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We release MiDaS v3.1 for monocular depth estimation, offering a variety ofnew models based on different encoder backbones. This release is motivated bythe success of transformers in computer vision, with a large variety ofpretrained vision transformers now available. We explore how using the mostpromising vision transformers as image encoders impacts depth estimationquality and runtime of the MiDaS architecture. Our investigation also includesrecent convolutional approaches that achieve comparable quality to visiontransformers in image classification tasks. While the previous release MiDaSv3.0 solely leverages the vanilla vision transformer ViT, MiDaS v3.1 offersadditional models based on BEiT, Swin, SwinV2, Next-ViT and LeViT. These modelsoffer different performance-runtime tradeoffs. The best model improves thedepth estimation quality by 28% while efficient models enable downstream tasksrequiring high frame rates. We also describe the general process forintegrating new backbones. A video summarizing the work can be found athttps://youtu.be/UjaeNNFf9sE and the code is available athttps://github.com/isl-org/MiDaS.

MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation