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Point-SAM: Promptable 3D Segmentation Model for Point Clouds
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The development of 2D foundation models for image segmentation has been significantly advanced by the Segment Anything Model (SAM). However, achieving similar success in 3D models remains a challenge due to issues such as non-unified data formats, poor model scalability, and the scarcity of labeled data with diverse masks. To this end, we propose a 3D promptable segmentation model Point-SAM, focusing on point clouds. We employ an efficient transformer-based architecture tailored for point clouds, extending SAM to the 3D domain. We then distill the rich knowledge from 2D SAM for Point-SAM training by introducing a data engine to generate part-level and object-level pseudo-labels at scale from 2D SAM. Our model outperforms state-of-the-art 3D segmentation models on several indoor and outdoor benchmarks and demonstrates a variety of applications, such as interactive 3D annotation and zero-shot 3D instance proposal. Codes and demo can be found at https://github.com/zyc00/Point-SAM.
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Cited by 7 Pith papers
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Industrial3D: A Water-Treatment TLS Point Cloud Dataset and Cross-Paradigm Benchmark for MEP Scene Understanding
A 612M-point industrial MEP TLS dataset and cross-paradigm benchmark show best supervised mIoU of 55.74% versus 15.79% zero-shot Point-SAM, a 39.95-point domain gap from 215:1 imbalance and cylindrical ambiguity.
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NegROI: Click-Centric Uncertainty-Guided Refinement with Scene-Conditioned Negative Prompts for Robust Interactive 3D Segmentation
NegROI couples click-centric multi-resolution ROI refinement with scene-conditioned negative prompts to reduce false positives and sharpen boundaries in interactive 3D point-cloud segmentation.
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Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes
A hierarchical neural-process model with scene- and object-level latent variables and a probabilistic prototype modulator improves click-based 3D segmentation and supplies per-point uncertainty maps.
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AffordDP: Generalizable Diffusion Policy with Transferable Affordance
A diffusion-based manipulation policy conditioned on transferred 3D contact points and post-contact trajectories, with adaptive affordance-guided sampling, generalizes to unseen object instances and categories.
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GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation
A prompt-controllable 3D part segmentation method that adapts SAM2 with LoRA and geometry fusion on rendered normal and point maps, then back-projects multi-view masks to the mesh.
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From Flight to Insight: Semantic 3D Reconstruction for Aerial Inspection via Gaussian Splatting and Language-Guided Segmentation
A hybrid pipeline using CLIP-LSeg heatmaps to point-prompt SAM performs language-guided segmentation on UAV Gaussian Splatting scenes, demonstrated qualitatively on two datasets.
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IPENS:Interactive Unsupervised Framework for Rapid Plant Phenotyping Extraction via NeRF-SAM2 Fusion
IPENS lifts SAM2's 2D segmentations into 3D via NeRF mask inverse rendering, enabling interactive multi-target point cloud extraction and trait estimation for rice and wheat.
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