Segment Any 3D Object with Language

• URL: http://arxiv.org/abs/2404.02157v1
• Date: Tue, 2 Apr 2024 17:59:10 GMT
• Title: Segment Any 3D Object with Language
• Authors: Seungjun Lee, Yuyang Zhao, Gim Hee Lee,
• Abstract summary: We introduce Segment any 3D Object with LanguagE (SOLE), a semantic geometric and-aware visual-language learning framework with strong generalizability. Specifically, we propose a multimodal fusion network to incorporate multimodal semantics in both backbone and decoder. Our SOLE outperforms previous methods by a large margin on ScanNetv2, ScanNet200, and Replica benchmarks.
• Score: 58.471327490684295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we investigate Open-Vocabulary 3D Instance Segmentation (OV-3DIS) with free-form language instructions. Earlier works that rely on only annotated base categories for training suffer from limited generalization to unseen novel categories. Recent works mitigate poor generalizability to novel categories by generating class-agnostic masks or projecting generalized masks from 2D to 3D, but disregard semantic or geometry information, leading to sub-optimal performance. Instead, generating generalizable but semantic-related masks directly from 3D point clouds would result in superior outcomes. In this paper, we introduce Segment any 3D Object with LanguagE (SOLE), which is a semantic and geometric-aware visual-language learning framework with strong generalizability by generating semantic-related masks directly from 3D point clouds. Specifically, we propose a multimodal fusion network to incorporate multimodal semantics in both backbone and decoder. In addition, to align the 3D segmentation model with various language instructions and enhance the mask quality, we introduce three types of multimodal associations as supervision. Our SOLE outperforms previous methods by a large margin on ScanNetv2, ScanNet200, and Replica benchmarks, and the results are even close to the fully-supervised counterpart despite the absence of class annotations in the training. Furthermore, extensive qualitative results demonstrate the versatility of our SOLE to language instructions.

Related papers

• RefMask3D: Language-Guided Transformer for 3D Referring Segmentation [32.11635464720755]
  RefMask3D aims to explore the comprehensive multi-modal feature interaction and understanding. RefMask3D outperforms previous state-of-the-art method by a large margin of 3.16% mIoU on the challenging ScanRefer dataset.
  arXiv  Detail & Related papers  (2024-07-25T17:58:03Z)
• 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)
• 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)
• 3D Open-Vocabulary Panoptic Segmentation with 2D-3D Vision-Language Distillation [40.49322398635262]
  We propose the first method to tackle 3D open-vocabulary panoptic segmentation. Our model takes advantage of the fusion between learnable LiDAR features and dense frozen vision CLIP features. We propose two novel loss functions: object-level distillation loss and voxel-level distillation loss.
  arXiv  Detail & Related papers  (2024-01-04T18:39:32Z)
• SAI3D: Segment Any Instance in 3D Scenes [68.57002591841034]
  We introduce SAI3D, a novel zero-shot 3D instance segmentation approach. Our method partitions a 3D scene into geometric primitives, which are then progressively merged into 3D instance segmentations. Empirical evaluations on ScanNet, Matterport3D and the more challenging ScanNet++ datasets demonstrate the superiority of our approach.
  arXiv  Detail & Related papers  (2023-12-17T09:05:47Z)
• Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation [67.07112533415116]
  We present a novel framework that adapts various foundational models for the 3D point cloud segmentation task. Our approach involves making initial predictions of 2D semantic masks using different large vision models. To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting.
  arXiv  Detail & Related papers  (2023-11-03T15:41:15Z)
• 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)
• Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions [70.0171362989609]
  We propose a novel fine-grained 3D shape classification method named FG3D-Net to capture the fine-grained local details of 3D shapes from multiple rendered views. Our results under the fine-grained 3D shape dataset show that our method outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-05-26T06:53:19Z)

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.