3D visual grounding is an emerging research area dedicated to making
connections between the 3D physical world and natural language, which is
crucial for achieving embodied intelligence. In this paper, we propose DASANet,
a Dual Attribute-Spatial relation Alignment Network that separately models and
aligns object attributes and spatial relation features between language and 3D
vision modalities. We decompose both the language and 3D point cloud input into
two separate parts and design a dual-branch attention module to separately
model the decomposed inputs while preserving global context in
attribute-spatial feature fusion by cross attentions. Our DASANet achieves the
highest grounding accuracy 65.1% on the Nr3D dataset, 1.3% higher than the best
competitor. Besides, the visualization of the two branches proves that our
method is efficient and highly interpretable.

提出了 DASANet，一种用于模型属性和空间关系特征的双重对齐网络，能够实现在语言和 3D 视觉模态之间建立联系，提高基于 3D 视觉的定位准确性。

3D 视觉定位的双属性空间关系对齐

Dual Attribute-Spatial Relation Alignment for 3D Visual Grounding

3D visual grounding aims to identify the target object within a 3D point
cloud scene referred to by a natural language description. While previous works
attempt to exploit the verbo-visual relation with proposed cross-modal
transformers, unstructured natural utterances and scattered objects might lead
to undesirable performances. In this paper, we introduce DOrA, a novel 3D
visual grounding framework with Order-Aware referring. DOrA is designed to
leverage Large Language Models (LLMs) to parse language description, suggesting
a referential order of anchor objects. Such ordered anchor objects allow DOrA
to update visual features and locate the target object during the grounding
process. Experimental results on the NR3D and ScanRefer datasets demonstrate
our superiority in both low-resource and full-data scenarios. In particular,
DOrA surpasses current state-of-the-art frameworks by 9.3% and 7.8% grounding
accuracy under 1% data and 10% data settings, respectively.

DOrA 是一个使用大型语言模型的 3D 视觉指向框架，通过引入有序锚定对象，更新视觉特征并定位目标对象，在低资源和全数据场景下表现出超越当前最先进框架的优越性，分别在 1％数据和 10％数据设置下将基准提高了 9.3％和 7.8％的准确率。