Knowledge Commons of Institute of Automation,CAS
Differentiable Convolution Search for Point Cloud Processing | |
Xing Nie1,2; Yongcheng Liu1; Shaohong Chen4; Jianlong Chang3; Chunlei Huo1; Gaofeng Meng1,2,5; Qi Tian3; Weiming Hu1; Chunhong Pan1 | |
2021-10 | |
会议名称 | IEEE/CVF International Conference on Computer Vision (ICCV) |
会议日期 | 2021年10月10日至2021年10月17日 |
会议地点 | Montreal, Canada |
摘要 | Exploiting convolutional neural networks for point cloud processing is quite challenging, due to the inherent irregular distribution and discrete shape representation of point clouds. To address these problems, many handcrafted convolution variants have sprung up in recent years. Though with elaborate design, these variants could be far from optimal in sufficiently capturing diverse shapes formed by discrete points. In this paper, we propose PointSeaConv, i.e., a novel differential convolution search paradigm on point clouds. It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling. We also propose a joint optimization framework for simultaneous search of internal convolution and external architecture, and introduce epsilon-greedy algorithm to alleviate the effect of discretization error. As a result, PointSeaNet, a deep network that is sufficient to capture geometric shapes at both convolution level and architecture level, can be searched out for point cloud processing. Extensive experiments strongly evidence that our proposed PointSeaNet surpasses current handcrafted deep models on challenging benchmarks across multiple tasks with remarkable margins. |
语种 | 英语 |
七大方向——子方向分类 | 三维视觉 |
国重实验室规划方向分类 | 环境多维感知 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57519 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. 2.School of Artificial Intelligence, University of Chinese Academy of Sciences. 3.Huawei Cloud & AI. 4.Xidian University. 5.Centre for Artificial Intelligence and Robotics, HK Institute of Science & Innovation, CAS. |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Xing Nie,Yongcheng Liu,Shaohong Chen,et al. Differentiable Convolution Search for Point Cloud Processing[C],2021. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Differentiable Convo(1249KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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