Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915304793178112 |
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| author | Liu, Jie Zhou, Pan Xiao, Zehao Shen, Jiayi Yin, Wenzhe Sonke, Jan-Jakob Gavves, Efstratios |
| author_facet | Liu, Jie Zhou, Pan Xiao, Zehao Shen, Jiayi Yin, Wenzhe Sonke, Jan-Jakob Gavves, Efstratios |
| contents | Interactive 3D segmentation has emerged as a promising solution for generating accurate object masks in complex 3D scenes by incorporating user-provided clicks. However, two critical challenges remain underexplored: (1) effectively generalizing from sparse user clicks to produce accurate segmentation, and (2) quantifying predictive uncertainty to help users identify unreliable regions. In this work, we propose NPISeg3D, a novel probabilistic framework that builds upon Neural Processes (NPs) to address these challenges. Specifically, NPISeg3D introduces a hierarchical latent variable structure with scene-specific and object-specific latent variables to enhance few-shot generalization by capturing both global context and object-specific characteristics. Additionally, we design a probabilistic prototype modulator that adaptively modulates click prototypes with object-specific latent variables, improving the model's ability to capture object-aware context and quantify predictive uncertainty. Experiments on four 3D point cloud datasets demonstrate that NPISeg3D achieves superior segmentation performance with fewer clicks while providing reliable uncertainty estimations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01726 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes Liu, Jie Zhou, Pan Xiao, Zehao Shen, Jiayi Yin, Wenzhe Sonke, Jan-Jakob Gavves, Efstratios Computer Vision and Pattern Recognition Interactive 3D segmentation has emerged as a promising solution for generating accurate object masks in complex 3D scenes by incorporating user-provided clicks. However, two critical challenges remain underexplored: (1) effectively generalizing from sparse user clicks to produce accurate segmentation, and (2) quantifying predictive uncertainty to help users identify unreliable regions. In this work, we propose NPISeg3D, a novel probabilistic framework that builds upon Neural Processes (NPs) to address these challenges. Specifically, NPISeg3D introduces a hierarchical latent variable structure with scene-specific and object-specific latent variables to enhance few-shot generalization by capturing both global context and object-specific characteristics. Additionally, we design a probabilistic prototype modulator that adaptively modulates click prototypes with object-specific latent variables, improving the model's ability to capture object-aware context and quantify predictive uncertainty. Experiments on four 3D point cloud datasets demonstrate that NPISeg3D achieves superior segmentation performance with fewer clicks while providing reliable uncertainty estimations. |
| title | Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.01726 |