3D Instance Segmentation Via Enhanced Spatial and Semantic Supervision.

3D instance segmentation has recently garnered increased attention. Typical deep learning methods adopt point grouping schemes followed by hand-designed geometric clustering. Inspired by the success of transformers for various 3D tasks, newer hybrid approaches have utilized transformer decoders coupled with convolutional backbones that operate on voxelized scenes. However, due to the nature of sparse feature backbones, the extracted features provided to the transformer decoder are lacking in spatial understanding. Thus, such approaches often predict spatially separate objects as single instances. To this end, we introduce a novel approach for 3D point clouds instance segmentation that addresses the challenge of generating distinct instance masks for objects that share similar appearances but are spatially separated. Our method leverages spatial and semantic supervision with query refinement to improve the performance of hybrid 3D instance segmentation models. Specifically, we provide the transformer block with spatial features to facilitate differentiation between similar object queries and incorporate semantic supervision to enhance prediction accuracy based on object class. Our proposed approach outperforms existing methods on the validation sets of ScanNet V2 and ScanNet200 datasets, establishing a new state-of-the-art for this task.