  The 3D visual grounding task has been explored with visual and language
streams comprehending referential language to identify target objects in 3D
scenes. However, most existing methods devote the visual stream to capturing
the 3D visual clues using off-the-shelf point clouds encoders. The main
question we address in this paper is "can we consolidate the 3D visual stream
by 2D clues synthesized from point clouds and efficiently utilize them in
training and testing?". The main idea is to assist the 3D encoder by
incorporating rich 2D object representations without requiring extra 2D inputs.
To this end, we leverage 2D clues, synthetically generated from 3D point
clouds, and empirically show their aptitude to boost the quality of the learned
visual representations. We validate our approach through comprehensive
experiments on Nr3D, Sr3D, and ScanRefer datasets and show consistent
performance gains compared to existing methods. Our proposed module, dubbed as
Look Around and Refer (LAR), significantly outperforms the state-of-the-art 3D
visual grounding techniques on three benchmarks, i.e., Nr3D, Sr3D, and
ScanRefer. The code is available at https://eslambakr.github.io/LAR.github.io/.
