  3D referring expression comprehension (3DREC) and segmentation (3DRES) have
overlapping objectives, indicating their potential for collaboration. However,
existing collaborative approaches predominantly depend on the results of one
task to make predictions for the other, limiting effective collaboration. We
argue that employing separate branches for 3DREC and 3DRES tasks enhances the
model's capacity to learn specific information for each task, enabling them to
acquire complementary knowledge. Thus, we propose the MCLN framework, which
includes independent branches for 3DREC and 3DRES tasks. This enables dedicated
exploration of each task and effective coordination between the branches.
Furthermore, to facilitate mutual reinforcement between these branches, we
introduce a Relative Superpoint Aggregation (RSA) module and an Adaptive Soft
Alignment (ASA) module. These modules significantly contribute to the precise
alignment of prediction results from the two branches, directing the module to
allocate increased attention to key positions. Comprehensive experimental
evaluation demonstrates that our proposed method achieves state-of-the-art
performance on both the 3DREC and 3DRES tasks, with an increase of 2.05% in
<EMAIL_ADDRESS>for 3DREC and 3.96% in mIoU for 3DRES.
