High Resolution Depth Maps for Stable Diffusion WebUI
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Updated
Aug 18, 2024 - Python
High Resolution Depth Maps for Stable Diffusion WebUI
Pytorch Implementation of Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Python CLI for generating depth map from stereoscopic image
Dense Depth Estimation from Multiple 360-degree Images Using Virtual Depth
Tensorflow implementation of Deep Ordinal Regression Network for Monocular Depth Estimation
Use an image segmentation to produce a RGB+D image (image + depthmap). Or use the GUI to view already-made RGB+D images in 3D, there's even an anaglyph mode to perceive depth with red+cyan glasses. Animate the 3D view and export to a series of images to build later an animated image. This nice GUI uses QT, OpenCV and OpenGL
Minecraft as a real-world hologram. No glasses required.
Monocular depth estimation from ArgoAI's Lidar based Depth dataset - Depth predictions up-to 200m
Disparity and depth maps GUI with QT and OpenCV with support for classic image files and MPO stereo pairs
A straightforward Siamese network designed for block matching to generate a disparity map
An example script that uses the objc_util module available in the Pythonista app to extract depth data from Portrait photos.
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