RefMask3D: Language-Guided Transformer for 3D Referring Segmentation (2407.18244v1)

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.

RefMask3D: Language-Guided Transformer for 3D Referring Segmentation

The paper "RefMask3D: Language-Guided Transformer for 3D Referring Segmentation" introduces a novel approach to the intricate task of 3D referring segmentation. This task involves segmenting a target object described by a natural language expression within a point cloud scene. The central challenge lies in efficiently fusing and aligning vision-language features, made difficult by the sparse and irregular nature of point clouds. RefMask3D tackles this through an innovative architecture that enhances multi-modal feature interaction and deeper semantic understanding.

Technical Contributions

The paper's standout contribution is the Geometry-Enhanced Group-Word Attention (GEGWA), which integrates language with geometrically coherent groups of points. This design choice leverages inherent geometric structures in point clouds to facilitate more effective cross-modal feature fusion early in the feature extraction process, mitigating the challenges presented by sparse data distribution.

Another essential development is the Linguistic Primitives Construction (LPC), responsible for generating primitives representing distinct semantic attributes. This methodology allows the model to gain a nuanced understanding of language at the decoding stage, essential for accurately identifying target objects based on complex linguistic descriptions.

An Object Cluster Module (OCM) further contributes by analyzing relationships among linguistic primitives, unifying insights, and pinpointing common characteristics. This comprehensive approach to vision-language feature fusion significantly enhances the precision of target identification, as evidenced by state-of-the-art performance results.

Numerical Results

RefMask3D showcased its efficacy with impressive numeric results, achieving new state-of-the-art performance across several tasks: 3D referring segmentation, 3D visual grounding, and even 2D referring image segmentation. Notably, it surpassed the previous best method on the ScanRefer dataset by a margin of 3.16% in mIoU, underscoring its capability in addressing challenges posed by 3D data structure.

Implications and Future Directions

The practical application of RefMask3D's approach extends beyond academic interest, potentially impacting areas such as augmented reality, autonomous driving, and robotic perception, where understanding complex object descriptions is vital. The theoretical implications suggest further exploration into vision-language fusion, especially in conjunction with 3D spatial data.

Future research could focus on optimizing computational requirements, making the model more accessible for deployment in resource-constrained environments. Moreover, investigating the model's adaptability to other modalities or incorporating other contextual information could broaden its application scope.

RefMask3D stands as a significant advancement in the field of vision-language tasks, primarily due to its innovative fusion strategies and comprehensive feature representation techniques. As 3D data becomes increasingly central to technology applications, methods like RefMask3D will be pivotal in furthering the capabilities of AI systems to interpret and interact with complex environments.