  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.
