RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding

• URL: http://arxiv.org/abs/2304.00962v4
• Date: Sun, 5 May 2024 04:44:55 GMT
• Title: RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding
• Authors: Jihan Yang, Runyu Ding, Weipeng Deng, Zhe Wang, Xiaojuan Qi,
• Abstract summary: We introduce a 3D-aware SFusion strategy that fuses 3D vision-language pairs derived from multiple 2D foundation models. We devise a region-aware point-discriminative contrastive learning objective to enable robust and effective 3D learning. Our model outperforms prior 3D open-world scene understanding approaches by an average of 17.2% and 9.1% for semantic and instance segmentation.
• Score: 46.253711788685536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a lightweight and scalable Regional Point-Language Contrastive learning framework, namely \textbf{RegionPLC}, for open-world 3D scene understanding, aiming to identify and recognize open-set objects and categories. Specifically, based on our empirical studies, we introduce a 3D-aware SFusion strategy that fuses 3D vision-language pairs derived from multiple 2D foundation models, yielding high-quality, dense region-level language descriptions without human 3D annotations. Subsequently, we devise a region-aware point-discriminative contrastive learning objective to enable robust and effective 3D learning from dense regional language supervision. We carry out extensive experiments on ScanNet, ScanNet200, and nuScenes datasets, and our model outperforms prior 3D open-world scene understanding approaches by an average of 17.2\% and 9.1\% for semantic and instance segmentation, respectively, while maintaining greater scalability and lower resource demands. Furthermore, our method has the flexibility to be effortlessly integrated with language models to enable open-ended grounded 3D reasoning without extra task-specific training. Code is available at https://github.com/CVMI-Lab/PLA.

Related papers

• 3D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidance [68.8825501902835]
  3DSS-VLG is a weakly supervised approach for 3D Semantic with 2D Vision-Language Guidance. To the best of our knowledge, this is the first work to investigate 3D weakly supervised semantic segmentation by using the textual semantic information of text category labels.
  arXiv  Detail & Related papers  (2024-07-13T09:39:11Z)
• Unlocking Textual and Visual Wisdom: Open-Vocabulary 3D Object Detection Enhanced by Comprehensive Guidance from Text and Image [70.02187124865627]
  Open-vocabulary 3D object detection (OV-3DDet) aims to localize and recognize both seen and previously unseen object categories within any new 3D scene. We leverage a vision foundation model to provide image-wise guidance for discovering novel classes in 3D scenes. We demonstrate significant improvements in accuracy and generalization, highlighting the potential of foundation models in advancing open-vocabulary 3D object detection.
  arXiv  Detail & Related papers  (2024-07-07T04:50:04Z)
• Grounded 3D-LLM with Referent Tokens [58.890058568493096]
  We propose Grounded 3D-LLM to consolidate various 3D vision tasks within a unified generative framework. The model uses scene referent tokens as special noun phrases to reference 3D scenes. Per-task instruction-following templates are employed to ensure natural and diversity in translating 3D vision tasks into language formats.
  arXiv  Detail & Related papers  (2024-05-16T18:03:41Z)
• UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation [46.998093729036334]
  We propose a unified multimodal 3D open-vocabulary scene understanding network, namely UniM-OV3D. To better integrate global and local features of the point clouds, we design a hierarchical point cloud feature extraction module. To facilitate the learning of coarse-to-fine point-semantic representations from captions, we propose the utilization of hierarchical 3D caption pairs.
  arXiv  Detail & Related papers  (2024-01-21T04:13:58Z)
• SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding [37.47195477043883]
  3D vision-language grounding, which focuses on aligning language with the 3D physical environment, stands as a cornerstone in the development of embodied agents. We introduce the first million-scale 3D vision-language dataset, SceneVerse, encompassing about 68K 3D indoor scenes. We demonstrate this scaling allows for a unified pre-training framework, Grounded Pre-training for Scenes (GPS) for 3D vision-language learning.
  arXiv  Detail & Related papers  (2024-01-17T17:04:35Z)
• Generalized Label-Efficient 3D Scene Parsing via Hierarchical Feature Aligned Pre-Training and Region-Aware Fine-tuning [55.517000360348725]
  This work presents a framework for dealing with 3D scene understanding when the labeled scenes are quite limited. To extract knowledge for novel categories from the pre-trained vision-language models, we propose a hierarchical feature-aligned pre-training and knowledge distillation strategy. Experiments with both indoor and outdoor scenes demonstrated the effectiveness of our approach in both data-efficient learning and open-world few-shot learning.
  arXiv  Detail & Related papers  (2023-12-01T15:47:04Z)
• Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding [57.47315482494805]
  Open-world instance-level scene understanding aims to locate and recognize unseen object categories that are not present in the annotated dataset. This task is challenging because the model needs to both localize novel 3D objects and infer their semantic categories. We propose to harness pre-trained vision-language (VL) foundation models that encode extensive knowledge from image-text pairs to generate captions for 3D scenes.
  arXiv  Detail & Related papers  (2023-08-01T07:50:14Z)
• Language-Assisted 3D Feature Learning for Semantic Scene Understanding [26.414294993374543]
  Language-assisted 3D feature learning can be combined with modern object detection and instance segmentation methods. Experiments on several benchmarks of 3D-only and 3D-language tasks demonstrate the effectiveness of our language-assisted 3D feature learning.
  arXiv  Detail & Related papers  (2022-11-25T13:21:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.