facebookresearch/sam3
Sam3
This project is a computer vision system for object segmentation and tracking across images and videos. It employs models capable of identifying and masking objects using text prompts, bounding boxes, click points, or image exemplars.
The system differentiates itself through memory-based video tracking and shared-memory architectures that maintain consistent object identities over time. It supports multi-object processing in single computation passes to increase frame throughput and utilizes iterative refinement to correct segmentation boundaries through sequential prompts.
The software also covers 3D object reconstruction, generating three-dimensional representations from two-dimensional visual data for spatial analysis.
Features
- Video Object Tracking - Maintains consistent object identities across video frames using a specialized temporal memory buffer.
- Joint Detection-Embedding Architectures - Implements joint architectures that process multiple object instances in a single computation pass for high throughput.
- Object Tracking Systems - Tracks multiple objects simultaneously using a shared-memory approach to maximize frame throughput.
- Image Segmentation - Offers interactive segmentation of images using prompts, boxes, and points for precise object isolation.
- Language-Based Segmentation - Identifies and masks object instances using noun phrases or image exemplars for concept-based segmentation.
- Prompt-Based Masking - Generates precise object masks using text descriptions, bounding boxes, or click points.
- Shared Spatial Memory - Uses a shared-memory architecture to maintain spatial consistency for multiple objects across video sequences.
- Image Segmentation - Utilizes language-based prompts and visual cues to identify and mask objects in images and video.
- Trajectory Consistency - Ensures stable object tracking over time by linking segmentation fragments into continuous trajectories.
- Mask Refinement Loops - Provides iterative boundary correction through sequential prompts to improve mask precision.
- Interactive Segmenters - Provides a tool for iteratively refining segmentation masks to correct errors via prompts.
- Concept-Based Masking - Detects and masks objects in images and videos using noun phrases or image exemplars.
- Interactive Mask Correction - Allows users to interactively correct segmentation errors and include missing object parts via prompts.
- High-Throughput Multi-Object Analysis - Increases frame throughput by processing several objects in a single computation pass.
- 3D Reconstruction - Generates three-dimensional representations of people and objects from two-dimensional visual data.
