论文标题
自动MVCNN：神经体系结构搜索多视图3D形状识别
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition
论文作者
论文摘要
在3D形状识别中，基于多视图的方法利用了人类的观点来分析3D形状，并取得了显着的结果。大多数现有的研究作品在深度学习中的作品采用手工制作的网络作为骨干，因为它们的特征提取能力很高，并且也受益于Imagenet预训练。但是，这些网络体系结构是否适合3D分析仍不清楚。在本文中，我们提出了一种名为Auto-MVCNN的神经体系结构搜索方法，该方法特别设计用于优化多视图3D形状识别的体系结构。 Auto-MVCNN通过自动搜索融合单元以探索视图特征之间的固有相关性来扩展基于梯度的框架以处理多视图图像。此外，我们开发了一种端到端方案，以通过权衡参数搜索来提高检索性能。广泛的实验结果表明，搜索的体系结构在各个方面都显着胜过手动设计的同行，我们的方法同时实现了最新的性能。
In 3D shape recognition, multi-view based methods leverage human's perspective to analyze 3D shapes and have achieved significant outcomes. Most existing research works in deep learning adopt handcrafted networks as backbones due to their high capacity of feature extraction, and also benefit from ImageNet pretraining. However, whether these network architectures are suitable for 3D analysis or not remains unclear. In this paper, we propose a neural architecture search method named Auto-MVCNN which is particularly designed for optimizing architecture in multi-view 3D shape recognition. Auto-MVCNN extends gradient-based frameworks to process multi-view images, by automatically searching the fusion cell to explore intrinsic correlation among view features. Moreover, we develop an end-to-end scheme to enhance retrieval performance through the trade-off parameter search. Extensive experimental results show that the searched architectures significantly outperform manually designed counterparts in various aspects, and our method achieves state-of-the-art performance at the same time.
