Question: What format of data does this work convert 3D point clouds to for segmentation?
Date: 2024-08-29

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ZiyuGuo99/SAM2Point: The Most Faithful Implementation of SAM in 3D - GitHub
We introduce SAM2Point, a preliminary exploration adapting Segment Anything Model 2 (SAM 2) for zero-shot and promptable 3D segmentation.Our framework supports various prompt types, including 3D points, boxes, and masks, and can generalize across diverse scenarios, such as 3D objects, indoor scenes, outdoor scenes, and raw LiDAR.. To our best knowledge, SAM2POINT presents the most faithful ...

SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners
Introduction. We introduce Sam2Point, a preliminary exploration adapting Segment Anything Model 2 (SAM 2) for zero-shot and promptable 3D segmentation. Our framework supports various prompt types, including 3D points, boxes, and masks, and can generalize across diverse scenarios, such as 3D objects, indoor scenes, outdoor scenes, and raw LiDAR.

SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners
We introduce SAM2Point, a preliminary exploration adapting Segment Anything Model 2 (SAM 2) for zero-shot and promptable 3D segmentation. SAM2Point interprets any 3D data as a series of multi-directional videos, and leverages SAM 2 for 3D-space segmentation, without further training or 2D-3D projection. Our framework supports various prompt types, including 3D points, boxes, and masks, and can ...

Sam2Point: Segment Any 3D as Videosin Zero-shot and Promptable Manners
Abstract. We introduce Sam2Point, a preliminary exploration adapting Segment Anything Model 2 (SAM 2) for zero-shot and promptable 3D segmentation. Sam2Point interprets any 3D data as a series of multi-directional videos, and leverages SAM 2 for 3D-space segmentation, without further training or 2D-3D projection. Our framework supports various prompt types, including 3D points, boxes, and ...

SAM2Point: A Preliminary Exploration Adapting Segment Anything Model 2 ...
At the core of SAM2POINT's innovation is its ability to format 3D data into voxelized representations resembling videos, allowing SAM 2 to perform zero-shot segmentation while preserving fine-grained spatial information. The voxelized 3D data is structured as w×h×l×3, where each voxel corresponds to a point in the 3D space.

Point-SAM: Promptable 3D Segmentation Model for Point Clouds
We provide pretrained checkpoint for Point-SAM with ViT-L on HuggingFace. After downloading the checkpoint, you can inference with the following codes. For different numbers of points, we allow custom group_number and group_size.The default setting is group_number=512 and group_size=64, and we suggest setting group_number=2048 and group_size=256 while the number of points > 100k.

Segment Anything 3D for Point Clouds: Complete Guide (SAM 3D)
9. The Segment Anything Model for 3D Environments. We will detect objects in indoor spaces using 3D point cloud datasets. Credit goes to Mimatelier, the talented illustrator who created this image. Technological leaps are just plain crazy, especially looking at Artificial Intelligence (AI) applied to 3D challenges.

Point-SAM: Promptable 3D Segmentation Model for Point Clouds - arXiv.org
Figure 1: We address 3 critical components for 3D promptable segmentation. First, we propose a 3D extension of SAM [], named Point-SAM, which predicts masks given the input point cloud and prompts.To scale up training data, we develop a data engine to generate pseudo labels, where SAM is employed to generate initial diverse mask proposals, and iteratively refine them with both SAM and our ...

Answer: Videos