Dense Object Grounding in 3D Scenes
Wencan Huang Daizong Liu Wei Hu
Localizing objects in 3D scenes according to the semantics of a given natural
language is a fundamental yet important task in the field of multimedia
understanding, which benefits various real-world applications such as robotics
and autonomous driving. However, the majority of existing 3D object grounding
methods are restricted to a single-sentence input describing an individual
object, which cannot comprehend and reason more contextualized descriptions of
multiple objects in more practical 3D cases. To this end, we introduce a new
challenging task, called 3D Dense Object Grounding (3D DOG), to jointly
localize multiple objects described in a more complicated paragraph rather than
a single sentence. Instead of naively localizing each sentence-guided object
independently, we found that dense objects described in the same paragraph are
often semantically related and spatially located in a focused region of the 3D
scene. To explore such semantic and spatial relationships of densely referred
objects for more accurate localization, we propose a novel Stacked Transformer
based framework for 3D DOG, named 3DOGSFormer. Specifically, we first devise a
contextual query-driven local transformer decoder to generate initial grounding
proposals for each target object. Then, we employ a proposal-guided global
transformer decoder that exploits the local object features to learn their
correlation for further refining initial grounding proposals. Extensive
experiments on three challenging benchmarks (Nr3D, Sr3D, and ScanRefer) show
that our proposed 3DOGSFormer outperforms state-of-the-art 3D single-object
grounding methods and their dense-object variants by significant margins.