Search Results for "mesh segmentation"

MediaPipe offers open-source cross-platform, customizable ML solutions for live and streaming media. Provides segmentation masks for prominent humans in the scene. MediaPipe Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning (ML) to infer the 3D surface geometry, requiring only a single camera input without the need for a dedicated depth sensor. Utilizing lightweight model architectures together with GPU acceleration throughout the pipeline, the solution delivers real-time performance-critical for live experiences. ...

NetBird is an open-source solution that builds a WireGuard®-based overlay network enabling seamless, encrypted peer-to-peer connectivity without the complexity of firewall rules, port forwarding, or centralized VPN gateways. It integrates access control features such as SSO and MFA for secure, policy-driven networking. Every machine in the network runs NetBird Agent (or Client) that manages WireGuard. Every agent connects to Management Service that holds network state, manages peer IPs, and...

...These capabilities make the architecture well suited for tasks such as 3D object classification, segmentation, and geometric analysis. The project provides training pipelines, dataset preparation tools, and visualization utilities to support experiments with mesh-based neural networks.

The VRN (Volumetric Regression Network) repository implements the “Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression” method. Instead of explicitly fitting a 3D model via landmark estimation and deformation, VRN treats the reconstruction task as volumetric segmentation: it learns a CNN to regress a 3D volume aligned to the input image, and then extracts a mesh via isosurface from that volume. The network is unguided (no 2D landmarks as intermediate). The mesh surfaces can be textured (in MATLAB branch) and colored. Docker container provided for easy CPU deployment.

GIMIAS is a workflow-oriented environment for solving advanced biomedical image computing and individualized simulation problems, which is extensible through the development of problem-specific plug-ins. In addition, GIMIAS provides an open source framework for efficient development of research and clinical software prototypes integrating contributions from the Physiome community while allowing business-friendly technology transfer and commercial product development.

This project aims to create a complete mesh sequence segmentation environment: a framework to create, test and compare mesh sequence segmentation algorithms. It will also provide building blocks to create these algorithms and a graphical environment.