depth-anything-v2

Official code for the paper: Depth Anything At Any Condition

an easy way to create Abrasion/Scratch/Hightlight Holography from 2D images/pictures;轻易从平面图像/照片创建刮擦全息路径

Optimizing Monocular Depth Estimation with TensorRT: Model Conversion, Inference Acceleration, and 3D Reconstruction

Tensorrt codebase to inference in c++ for all major neural arch using onnx

M2Retinexformer: Multi-Modal Retinexformer for Low-Light Image Enhancement [IEEE ICIP 2026] is a novel framework that extends Retinexformer by incorporating depth cues, luminance priors, and semantic features within a progressive refinement pipeline.

Ready-to-deploy models including Segment Anything 3, Depth Anything 2 and Gemma.

[AI POC - STATUS: WORKING AT FULL SPEED, BUT POOR HALOING AND EDGE ALIGNMENT] DirectShow filter to convert 2D videos to 3D SBS for common media players (which support external filters) in real-time. Made with Claude.

Turn any video into depth maps, pose skeletons and 478-point face clouds for AI motion control (Seedance 2.0 / Kling / Runway) — 100% in-browser, no install, no upload.

Comparative analysis of monocular depth estimation methods (ResNet-50, frozen Stable Diffusion UNet, I-JEPA, SD+I-JEPA fusion, DepthAnything V2) with robustness evaluation under fog, blur, and low-light on NYU Depth V2.

Transform 2D images into 3D worlds using deep learning. This small project explores monocular depth estimation and 3D point cloud generation from a single image.

A pipeline to identify optimal picking surfaces on packages and estimate their surface normals for robotic manipulation. by using Depth Anything V2 and Segment Anything Model - SAM.

Convert images, PNG sequences and videos into MagicEye autostereograms. AI depth via MiDaS / Depth Anything V2. PySide6 UI inspired by X.

Monocular-only autonomous indoor navigation for low-cost search-and-rescue drones: appearance-free door entry and TRISTAR tri-signal stair climbing on a DJI Tello.

Comparison of Depth Anything V2 and MiDaS for indoor monocular depth estimation and obstacle detection on NYU Depth V2.

Real-time monocular depth estimation and GPU visual effects using Depth Anything V2, PyTorch, CUDA, OpenGL, and GLSL. Converts webcam video into depth-aware fog, blur, lighting, and heatmap effects, with a Streamlit dashboard for live visualization, controls, telemetry, and performance monitoring.

ROS 2 (Humble) package wrapping three interchangeable monocular depth estimation models — YOLO26 Depth, UniDepth V2, and Depth Anything V2 — using stock pretrained weights, with a fine-tuned companion repo available for NYU Depth V2/ScanNet-adapted checkpoints.

Shared offline evaluation toolkit for depth estimation checkpoints — YOLO26-Depth and Depth Anything V2, PyTorch and OpenVINO, ScanNet benchmarks and local lab-generalization tests, one consistent metric pipeline throughout.

Fine-tuned version of Multi-Model-Monocular-Depth-Estimation-for-ROS-2 — ROS 2 (Humble) package for YOLO26 Depth and Depth Anything V2, using checkpoints fine-tuned on NYU Depth V2 and ScanNet (hosted on Hugging Face) instead of base pretrained weights.

MassingPro is a lightweight, zero-compute architectural utility that converts 2D facade photographs into UV-mapped 3D context models. It bypasses complex 3D topology by generating 16-bit displacement and normal maps, allowing for high-fidelity architectural visualization at zero CAD modeling cost.