CO-Net++:一次完成多个点云任务的内聚网络,带两阶段特征校正。

Tao Xie;Kun Dai;Qihao Sun;Zhiqiang Jiang;Chuqing Cao;Lijun Zhao;Ke Wang;Ruifeng Li
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引用次数: 0

摘要

我们提出了 CO-Net++,这是一个内聚性框架,采用两阶段特征校正策略,在异构数据集领域对多个点云任务进行集体优化。CO-Net++ 的核心在于优化任务共享参数,以捕捉不同任务的通用特征,同时辨别特定任务参数,以概括每个任务的独特特征。具体来说,CO-Net++ 开发了一种两阶段特征修正策略(TFRS),将任务共享参数和任务特定参数的优化过程截然分开。在第一阶段,TFRS 将骨干网中的所有参数配置为任务共享参数,从而鼓励 CO-Net++ 彻底吸收与所有任务相关的通用属性。此外,TFRS 还引入了基于符号的梯度手术,以促进任务共享参数的优化,从而缓解不同数据集域引起的梯度冲突。在第二阶段,TFRS 会冻结任务共享参数,并灵活地将特定任务参数整合到网络中,以编码每个数据集域的具体特征。CO-Net++ 显著缓解了因参数纠缠而产生的优化冲突,确保了通用特征和特定特征的充分识别。广泛的实验表明,CO-Net++ 在三维物体检测和三维语义分割任务中均表现出色。此外,CO-Net++ 还具有令人印象深刻的增量学习能力,在推广到新的点云任务时可防止灾难性失忆。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CO-Net++: A Cohesive Network for Multiple Point Cloud Tasks at Once With Two-Stage Feature Rectification
We present CO-Net++, a cohesive framework that optimizes multiple point cloud tasks collectively across heterogeneous dataset domains with a two-stage feature rectification strategy. The core of CO-Net++ lies in optimizing task-shared parameters to capture universal features across various tasks while discerning task-specific parameters tailored to encapsulate the unique characteristics of each task. Specifically, CO-Net++ develops a two-stage feature rectification strategy (TFRS) that distinctly separates the optimization processes for task-shared and task-specific parameters. At the first stage, TFRS configures all parameters in backbone as task-shared, which encourages CO-Net++ to thoroughly assimilate universal attributes pertinent to all tasks. In addition, TFRS introduces a sign-based gradient surgery to facilitate the optimization of task-shared parameters, thus alleviating conflicting gradients induced by various dataset domains. In the second stage, TFRS freezes task-shared parameters and flexibly integrates task-specific parameters into the network for encoding specific characteristics of each dataset domain. CO-Net++ prominently mitigates conflicting optimization caused by parameter entanglement, ensuring the sufficient identification of universal and specific features. Extensive experiments reveal that CO-Net++ realizes exceptional performances on both 3D object detection and 3D semantic segmentation tasks. Moreover, CO-Net++ delivers an impressive incremental learning capability and prevents catastrophic amnesia when generalizing to new point cloud tasks.
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