LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds

Chenxi Liu, Zhaoqi Leng, Peigen Sun, Shuyang Cheng, C. Qi, Yin Zhou, Mingxing Tan, Drago Anguelov
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引用次数: 4

Abstract

Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving. However, arguably due to the higher-dimensional nature of the data (as compared to images), existing neural architectures exhibit a large variety in their designs, including but not limited to the views considered, the format of the neural features, and the neural operations used. Lack of a unified framework and interpretation makes it hard to put these designs in perspective, as well as systematically explore new ones. In this paper, we begin by proposing a unified framework of such, with the key idea being factorizing the neural networks into a series of view transforms and neural layers. We demonstrate that this modular framework can reproduce a variety of existing works while allowing a fair comparison of backbone designs. Then, we show how this framework can easily materialize into a concrete neural architecture search (NAS) space, allowing a principled NAS-for-3D exploration. In performing evolutionary NAS on the 3D object detection task on the Waymo Open Dataset, not only do we outperform the state-of-the-art models, but also report the interesting finding that NAS tends to discover the same macro-level architecture concept for both the vehicle and pedestrian classes.
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LidarNAS:统一和搜索三维点云的神经架构
开发能够准确理解3D点云中的物体的神经模型对于机器人和自动驾驶的成功至关重要。然而,由于数据的高维性质(与图像相比),现有的神经架构在其设计中表现出多种多样,包括但不限于所考虑的视图、神经特征的格式和所使用的神经操作。由于缺乏统一的框架和解释,很难正确地看待这些设计,也很难系统地探索新的设计。在本文中,我们首先提出了一个统一的框架,其关键思想是将神经网络分解为一系列视图变换和神经层。我们证明,这种模块化框架可以重现各种现有的作品,同时允许主干设计的公平比较。然后,我们展示了这个框架如何容易地实现到一个具体的神经架构搜索(NAS)空间,允许一个原则性的NAS-for- 3d探索。在Waymo开放数据集上对3D目标检测任务执行渐进式NAS时,我们不仅优于最先进的模型,而且还报告了一个有趣的发现,即NAS倾向于为车辆和行人类别发现相同的宏观层面架构概念。
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