LidarNAS:统一和搜索三维点云的神经架构

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

摘要

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