Contrastive Mask Denoising Transformer for 3D Instance Segmentation

In transformer-based methods for point cloud instance segmentation, bipartite matching is used to establish one-to-one correspondences between predictions and ground truths. However, in early training stages, matches can be unstable and inconsistent between epochs, requiring the model to frequently adjust its learning path, thus reducing the quality of model convergence. To address this challenge, we propose the contrastive mask denoising transformer for 3D instance segmentation, which utilizes a mask denoising module to guide the model towards a more stable optimization path in early training stages. Furthermore, we introduce a multi-pattern-aware query selection module to assist the model learn multiple patterns at one position such that clustered objects can be discerned. In addition, the proposed modules are "plug and play", which can easily be integrated into transformer-based architectures. Experimental results on ScanNetv2 dataset show that the proposed modules improve the performance of multiple pipelines, notably achieving +1.0 mAP on the main pipeline.