  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 four novel stand-alone modules that 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, we then resolve view-dependent utterances via a learned
global camera token, and finally we employ multi-view ensembling to improve
referred mask quality. ConcreteNet ranks 1st on the challenging ScanRefer
online benchmark and has won the ICCV 3rd Workshop on Language for 3D Scenes
"3D Object Localization" challenge. Our code is available at
ouenal.github.io/concretenet/.
