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.Comment: Winner of the ICCV 2023 ScanRefer Challenge. This work has been
submitted to the IEEE for possible publication. Copyright may be transferred
without notice, after which this version may no longer be accessibl