# Monocular Depth Estimation

Distill-Any-Depth is a SOTA monocular depth estimation model trained based on knowledge distillation algorithms, capable of efficient and accurate depth estimation.

DepthPro is a vision model for depth estimation, capable of predicting scene depth information from a single image.

A monocular depth estimation model based on the Apache-2.0 license, suitable for zero-shot depth estimation tasks in wild scenes.

Depth Anything V2 is currently the most powerful monocular depth estimation model, trained on 595,000 synthetically annotated images and over 62 million real unlabeled images, offering finer details and stronger robustness.

Depth Anything V2 is currently the most powerful Monocular Depth Estimation (MDE) model, trained on 595,000 synthetically annotated images and over 62 million real unlabeled images, offering finer details and stronger robustness.

Depth Anything V2 is currently the most powerful monocular depth estimation model, trained on 595,000 synthetically annotated images and over 62 million real unlabeled images, featuring fine details and robustness.

Depth-Anything-V2-Base is an ONNX-format depth estimation model adapted for Transformers.js, designed for image depth estimation on the web.

Depth Anything V2 is currently the most powerful monocular depth estimation (MDE) model, trained on 595,000 synthetically annotated images and over 62 million real unannotated images.

ZoeDepth is a vision model for monocular depth estimation, fine-tuned on the KITTI dataset, capable of achieving zero-shot transfer for metric depth estimation.

A monocular depth estimation model fine-tuned using latent consistency distillation for generating depth maps from single images

ONNX version of depth estimation model based on Transformers.js, suitable for web applications

Depth Anything is a depth estimation model trained on large-scale unlabeled data, capable of predicting depth information from a single image.

DPT is a dense prediction transformer model based on the BEiT backbone network, designed for monocular depth estimation and trained on 1.4 million images.