facebookresearch/sam3

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.

Star history