TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Papers/MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative De...

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

Reiner Birkl, Diana Wofk, Matthias Müller

2023-07-26Image ClassificationDepth EstimationMonocular Depth Estimation
PaperPDFCode(official)Code

Abstract

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.

Results

TaskDatasetMetricValueModel
Depth EstimationETH3DDelta < 1.250.867MiDaS 3.1
Depth EstimationETH3Dabsolute relative error0.0139MiDaS 3.1
3DETH3DDelta < 1.250.867MiDaS 3.1
3DETH3Dabsolute relative error0.0139MiDaS 3.1

Related Papers

Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations2025-07-18Adversarial attacks to image classification systems using evolutionary algorithms2025-07-17Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy2025-07-17Federated Learning for Commercial Image Sources2025-07-17MUPAX: Multidimensional Problem Agnostic eXplainable AI2025-07-17$S^2M^2$: Scalable Stereo Matching Model for Reliable Depth Estimation2025-07-17$π^3$: Scalable Permutation-Equivariant Visual Geometry Learning2025-07-17Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation2025-07-16