3D point cloud visual grounding plays a critical role in 3D scene
comprehension, encompassing 3D referring expression comprehension (3DREC) and
segmentation (3DRES). We argue that 3DREC and 3DRES should be unified in one
framework, which is also a natural progression in the community. To explain,
3DREC can help 3DRES locate the referent, while 3DRES can also facilitate 3DREC
via more finegrained language-visual alignment. To achieve this, this paper
takes the initiative step to integrate 3DREC and 3DRES into a unified
framework, termed 3D Referring Transformer (3DRefTR). Its key idea is to build
upon a mature 3DREC model and leverage ready query embeddings and visual tokens
from the 3DREC model to construct a dedicated mask branch. Specially, we
propose Superpoint Mask Branch, which serves a dual purpose: i) By leveraging
the heterogeneous CPU-GPU parallelism, while the GPU is occupied generating
visual tokens, the CPU concurrently produces superpoints, equivalently
accomplishing the upsampling computation; ii) By harnessing on the inherent
association between the superpoints and point cloud, it eliminates the heavy
computational overhead on the high-resolution visual features for upsampling.
This elegant design enables 3DRefTR to achieve both well-performing 3DRES and
3DREC capacities with only a 6% additional latency compared to the original
3DREC model. Empirical evaluations affirm the superiority of 3DRefTR.
Specifically, on the ScanRefer dataset, 3DRefTR surpasses the state-of-the-art
3DRES method by 12.43% in mIoU and improves upon the SOTA 3DREC method by 0.6%
Acc@0.25IoU.

3D point cloud visual grounding encompasses 3D referring expression comprehension (3DREC) and segmentation (3DRES), and this paper proposes a unified framework called 3D Referring Transformer (3DRefTR) that integrates 3DREC and 3DRES, achieving superior performance on the ScanRefer dataset.