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Depth Anything V2 Metric Indoor Small Hf

Developed by depth-anything
A model fine-tuned from Depth Anything V2 for indoor metric depth estimation tasks, trained on the synthetic dataset Hypersim, compatible with the transformers library.
Downloads 750
Release Time : 7/26/2024

Model Overview

This model specializes in metric depth estimation for indoor scenes, capable of predicting absolute depth values for each pixel in an image, suitable for applications such as indoor 3D reconstruction and robot navigation.

Model Features

Indoor Metric Depth Estimation
Optimized for absolute depth prediction in indoor scenes
Synthetic Data Training
Trained on the Hypersim synthetic dataset to enhance model generalization
Transformers Compatibility
Directly usable via the Hugging Face Transformers library
Multiple Size Options
Available in Small, Base, and Large parameter sizes

Model Capabilities

Indoor scene depth estimation
Absolute depth prediction
Image depth map generation

Use Cases

3D Reconstruction
Indoor Scene 3D Modeling
Generates depth information from a single indoor image to assist in 3D scene reconstruction
Robot Navigation
Indoor Environment Perception
Provides environmental depth information for service robots to assist in obstacle avoidance and path planning
Augmented Reality
AR Scene Understanding
Supplies scene depth information for augmented reality applications to enable more realistic virtual object placement
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