Few Sampling Meshes-based 3D Tooth Segmentation via Region-Aware Graph Convolutional Network

Codes for MICCAI 2023 paper: 3D Dental Mesh Segmentation Using Semantics-Based Feature Learning with Graph-Transformer

Open-source Monocular Python HawkEye for Tennis

PasteBar - Limitless, Free Clipboard Manager for Mac and Windows

[CVPR 2023] Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis

[NeurIPS'22] PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

This is an unofficial implementation of the Point Transformer paper.

3D Dental surface segmentation with Tooth Group Network

iMeshSegNet implementations.

Subdivision-based Mesh Convolutional Networks.

PyTorch version of MeshSegNet for tooth segmentation of intraoral scans (point cloud/mesh). The code also includes visdom for training visualization; this project is partially powered by SOVE Inc.

Effortless data labeling with AI support from Segment Anything and other awesome models.

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Segment Anything in Medical Images

A clean and concise Python implementation of SIFT (Scale-Invariant Feature Transform)