论文标题
在点云中用于3D对象检测的本地网格渲染网络
Local Grid Rendering Networks for 3D Object Detection in Point Clouds
论文作者
论文摘要
3D对象检测模型在点云上的性能高度取决于它们对局部几何模式建模的能力。传统的基于点的模型通过对称函数（例如最大池）或基于图来利用局部模式，这很容易导致损失细粒的几何结构。关于捕获空间模式，CNN功能强大，但是在将整个点云量化为密集的常规3D网格之后，直接对点数据进行卷积在计算上是昂贵的。在这项工作中，我们旨在通过利用CNN来增强其模式学习能力，同时保持计算效率，从而提高基于点的模型的性能。我们提出了一个新颖的本地网格渲染（LGR）操作，以将一部分输入点的小社区置于低分辨率的3D网格中，从而使小型CNN可以准确地对本地模式进行建模，并避免在密集的网格上节省计算成本上的卷积。通过LGR操作，我们引入了一个新的通用骨干，称为LGR-NET，用于点云提取，并具有简单的设计和高效率。我们验证LGR-NET在挑战性的扫描板和Sun RGB-D数据集上进行3D对象检测。它分别在5.5和4.5地图上显着提高了最新的结果，只有略有增加的计算开销。
The performance of 3D object detection models over point clouds highly depends on their capability of modeling local geometric patterns. Conventional point-based models exploit local patterns through a symmetric function (e.g. max pooling) or based on graphs, which easily leads to loss of fine-grained geometric structures. Regarding capturing spatial patterns, CNNs are powerful but it would be computationally costly to directly apply convolutions on point data after voxelizing the entire point clouds to a dense regular 3D grid. In this work, we aim to improve performance of point-based models by enhancing their pattern learning ability through leveraging CNNs while preserving computational efficiency. We propose a novel and principled Local Grid Rendering (LGR) operation to render the small neighborhood of a subset of input points into a low-resolution 3D grid independently, which allows small-size CNNs to accurately model local patterns and avoids convolutions over a dense grid to save computation cost. With the LGR operation, we introduce a new generic backbone called LGR-Net for point cloud feature extraction with simple design and high efficiency. We validate LGR-Net for 3D object detection on the challenging ScanNet and SUN RGB-D datasets. It advances state-of-the-art results significantly by 5.5 and 4.5 mAP, respectively, with only slight increased computation overhead.
