RefMask3D: Language-Guided Transformer for 3D Referring Segmentation
- URL: http://arxiv.org/abs/2407.18244v1
- Date: Thu, 25 Jul 2024 17:58:03 GMT
- Title: RefMask3D: Language-Guided Transformer for 3D Referring Segmentation
- Authors: Shuting He, Henghui Ding,
- Abstract summary: 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.
- Score: 32.11635464720755
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 3D referring segmentation is an emerging and challenging vision-language task that aims to segment the object described by a natural language expression in a point cloud scene. The key challenge behind this task is vision-language feature fusion and alignment. In this work, we propose RefMask3D to explore the comprehensive multi-modal feature interaction and understanding. First, we propose a Geometry-Enhanced Group-Word Attention to integrate language with geometrically coherent sub-clouds through cross-modal group-word attention, which effectively addresses the challenges posed by the sparse and irregular nature of point clouds. Then, we introduce a Linguistic Primitives Construction to produce semantic primitives representing distinct semantic attributes, which greatly enhance the vision-language understanding at the decoding stage. Furthermore, we introduce an Object Cluster Module that analyzes the interrelationships among linguistic primitives to consolidate their insights and pinpoint common characteristics, helping to capture holistic information and enhance the precision of target identification. The proposed RefMask3D achieves new state-of-the-art performance on 3D referring segmentation, 3D visual grounding, and also 2D referring image segmentation. Especially, RefMask3D outperforms previous state-of-the-art method by a large margin of 3.16% mIoU} on the challenging ScanRefer dataset. Code is available at https://github.com/heshuting555/RefMask3D.
Related papers
- 3D-GRES: Generalized 3D Referring Expression Segmentation [77.10044505645064]
3D Referring Expression (3D-RES) is dedicated to segmenting a specific instance within a 3D space based on a natural language description.
Generalized 3D Referring Expression (3D-GRES) extends the capability to segment any number of instances based on natural language instructions.
arXiv Detail & Related papers (2024-07-30T08:59:05Z) - Segment Any 3D Object with Language [58.471327490684295]
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.
arXiv Detail & Related papers (2024-04-02T17:59:10Z) - SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph
Attention [19.23636231942245]
We propose a semantic-enhanced relational learning model based on a graph network with our designed memory graph attention layer.
Our method replaces original language-independent encoding with cross-modal encoding in visual analysis.
Experimental results on ReferIt3D and ScanRefer benchmarks show that the proposed method outperforms the existing state-of-the-art methods.
arXiv Detail & Related papers (2024-03-13T02:11:04Z) - 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) - Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers [65.51132104404051]
We introduce the use of object identifiers and object-centric representations to interact with scenes at the object level.
Our model significantly outperforms existing methods on benchmarks including ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.
arXiv Detail & Related papers (2023-12-13T14:27:45Z) - 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) - Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with
Foundation Models [18.315856283440386]
Foundation models have achieved remarkable results in 2D and language tasks like image segmentation, object detection, and visual-language understanding.
Their potential to enrich 3D scene representation learning is largely untapped due to the existence of the domain gap.
We propose an innovative methodology called Bridge3D to address this gap by pre-training 3D models using features, semantic masks, and sourced captions from foundation models.
arXiv Detail & Related papers (2023-05-15T16:36:56Z) - TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D
Visual Grounding [15.617150859765024]
We exploit Transformer for its natural suitability on permutation-invariant 3D point clouds data.
We propose a TransRefer3D network to extract entity-and-relation aware multimodal context.
Our proposed model significantly outperforms existing approaches by up to 10.6%.
arXiv Detail & Related papers (2021-08-05T05:47:12Z) - Locate then Segment: A Strong Pipeline for Referring Image Segmentation [73.19139431806853]
Referring image segmentation aims to segment the objects referred by a natural language expression.
Previous methods usually focus on designing an implicit and recurrent interaction mechanism to fuse the visual-linguistic features to directly generate the final segmentation mask.
We present a "Then-Then-Segment" scheme to tackle these problems.
Our framework is simple but surprisingly effective.
arXiv Detail & Related papers (2021-03-30T12:25:27Z)
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.