mlsanityData to 7 Oct 2026

Language-Level Semantics Conditioned 3D Point Cloud Segmentation

arXiv 2107.00430 ↗First posted 1 Jul 2021cs.CVcited by 7

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Abstract (arXiv)

In this work, a language-level Semantics Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to condition the modeling of point feature distribution as well as the pseudo-feature generation, and a feature-geometry-based mixup approach is further proposed to facilitate the distribution learning. To our knowledge, this is the first attempt in literature to introduce language-level semantics to the 3D point cloud segmentation task. Since a large number of point features could be generated from the learned distribution thanks to the semantics conditioned modeling, any existing segmentation network could be embedded into the proposed framework to boost its performance. In addition, the proposed framework has the inherent advantage of dealing with novel classes, which seems an impossible feat for the current segmentation networks. Extensive experimental results on two public datasets demonstrate that three typical segmentation networks could achieve significant improvements over their original performances after enhancement by the proposed framework in the conventional 3D segmentation task. Two benchmarks are also introduced for a newly introduced zero-shot 3D segmentation task, and the results also validate the proposed framework.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 46 of its 68 references that are in the map. First posted on arXiv 1 Jul 2021.

Builds on

46 of 68 references.

References · 46

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34 more
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 2.

7 papers.

  1. 2022OpenScene: 3D Scene Understanding with Open Vocabulariescited by 556
  2. 2023A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Futurecited by 59
  3. 2023CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detectioncited by 51
  4. 2023OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detectioncited by 22
  5. 2023See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Datacited by 21
  6. 2024Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detectioncited by 17
  7. 2024Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Modelscited by 12