3D visual grounding is the task of localizing the object in a 3D scene which
is referred by a description in natural language. With a wide range of
applications ranging from autonomous indoor robotics to AR/VR, the task has
recently risen in popularity. A common formulation to tackle 3D visual
grounding is grounding-by-detection, where localization is done via bounding
boxes. However, for real-life applications that require physical interactions,
a bounding box insufficiently describes the geometry of an object. We therefore
tackle the problem of dense 3D visual grounding, i.e. referral-based 3D
instance segmentation. We propose a dense 3D grounding network ConcreteNet,
featuring three novel stand-alone modules which aim to improve grounding
performance for challenging repetitive instances, i.e. instances with
distractors of the same semantic class. First, we introduce a bottom-up
attentive fusion module that aims to disambiguate inter-instance relational
cues, next we construct a contrastive training scheme to induce separation in
the latent space, and finally we resolve view-dependent utterances via a
learned global camera token. ConcreteNet ranks 1st on the challenging ScanRefer
online benchmark by a considerable +9.43% accuracy at 50% IoU and has won the
ICCV 3rd Workshop on Language for 3D Scenes "3D Object Localization" challenge.

通过引入密集三维视觉接地网络 ConcreteNet，该文研究了在物理交互类应用中实现密集三维视觉接地的方法，包括引入底层注意融合模块、对潜在空间进行对比训练、以及解决视角依赖性表达的全局相机令牌，提高了富有挑战性的重复实例的接地性能，从而实现了在三维场景中进行的交互式物体定位。