分割一切并不够，还要3D重建一切，SAM 3D来了

Core Insights - Meta has launched significant updates with the introduction of SAM 3D and SAM 3, enhancing the understanding of images in 3D [1][2] Group 1: SAM 3D Overview - SAM 3D is the latest addition to the SAM series, featuring two models that convert static 2D images into detailed 3D reconstructions [2][5] - SAM 3D Objects focuses on object and scene reconstruction, while SAM 3D Body specializes in human shape and pose estimation [5][28] - Meta has made the model weights and inference code for SAM 3D and SAM 3 publicly available [7] Group 2: SAM 3D Objects - SAM 3D Objects introduces a novel technical approach for robust and realistic 3D reconstruction and object pose estimation from a single natural image [11] - The model can generate detailed 3D shapes, textures, and scene layouts from everyday photos, overcoming challenges like small objects and occlusions [12][13] - Meta has annotated nearly 1 million images, generating approximately 3.14 million 3D meshes, leveraging a scalable data engine for efficient data collection [17][22] Group 3: SAM 3D Body - SAM 3D Body addresses the challenge of accurate human 3D pose and shape reconstruction from a single image, even in complex scenarios [28] - The model supports interactive input, allowing users to guide and control predictions for improved accuracy [29] - A high-quality training dataset of around 8 million images was created to enhance the model's performance across various 3D benchmarks [31] Group 4: SAM 3 Capabilities - SAM 3 introduces promptable concept segmentation, enabling the model to identify and segment instances of specific concepts based on text or example images [35] - The architecture of SAM 3 builds on previous AI advancements, utilizing Meta Perception Encoder for enhanced image recognition and object detection [37] - SAM 3 has achieved a twofold improvement in concept segmentation performance compared to existing models, with rapid inference times even for images with numerous detection targets [39]