DQFormer: Towards Unified LiDAR Panoptic Segmentation with Decoupled Queries

• URL: http://arxiv.org/abs/2408.15813v1
• Date: Wed, 28 Aug 2024 14:14:33 GMT
• Title: DQFormer: Towards Unified LiDAR Panoptic Segmentation with Decoupled Queries
• Authors: Yu Yang, Jianbiao Mei, Liang Liu, Siliang Du, Yilin Xiao, Jongwon Ra, Yong Liu, Xiao Xu, Huifeng Wu,
• Abstract summary: We propose a novel framework dubbed DQFormer to implement semantic and instance segmentation in a unified workflow. Specifically, we design a decoupled query generator to propose informative queries with semantics by localizing things/stuff positions. We also introduce a query-oriented mask decoder to decode corresponding segmentation masks.
• Score: 14.435906383301555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LiDAR panoptic segmentation, which jointly performs instance and semantic segmentation for things and stuff classes, plays a fundamental role in LiDAR perception tasks. While most existing methods explicitly separate these two segmentation tasks and utilize different branches (i.e., semantic and instance branches), some recent methods have embraced the query-based paradigm to unify LiDAR panoptic segmentation. However, the distinct spatial distribution and inherent characteristics of objects(things) and their surroundings(stuff) in 3D scenes lead to challenges, including the mutual competition of things/stuff and the ambiguity of classification/segmentation. In this paper, we propose decoupling things/stuff queries according to their intrinsic properties for individual decoding and disentangling classification/segmentation to mitigate ambiguity. To this end, we propose a novel framework dubbed DQFormer to implement semantic and instance segmentation in a unified workflow. Specifically, we design a decoupled query generator to propose informative queries with semantics by localizing things/stuff positions and fusing multi-level BEV embeddings. Moreover, a query-oriented mask decoder is introduced to decode corresponding segmentation masks by performing masked cross-attention between queries and mask embeddings. Finally, the decoded masks are combined with the semantics of the queries to produce panoptic results. Extensive experiments on nuScenes and SemanticKITTI datasets demonstrate the superiority of our DQFormer framework.

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