论文标题
端到端学习3D点云的本地多视图描述符
End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
论文作者
论文摘要
在这项工作中，我们提出了一个端到端框架，以了解3D点云的本地多视图描述符。为了采用类似的多视图表示，现有研究使用手工制作的观点在预处理阶段进行渲染，该阶段与随后的描述符学习阶段分离。在我们的框架中，我们通过使用可区分的渲染器将多视图渲染集成到神经网络中，该渲染器允许观点是优化的参数，以捕获更有用的局部兴趣点上下文。为了获得歧视性描述符，我们还设计了一个软视图池模块，以跨视图融合卷积功能。对现有3D注册基准的广泛实验表明，我们的方法在定量和质量上都优于现有的本地描述符。
In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds. To adopt a similar multi-view representation, existing studies use hand-crafted viewpoints for rendering in a preprocessing stage, which is detached from the subsequent descriptor learning stage. In our framework, we integrate the multi-view rendering into neural networks by using a differentiable renderer, which allows the viewpoints to be optimizable parameters for capturing more informative local context of interest points. To obtain discriminative descriptors, we also design a soft-view pooling module to attentively fuse convolutional features across views. Extensive experiments on existing 3D registration benchmarks show that our method outperforms existing local descriptors both quantitatively and qualitatively.
