1 research outputs found
Local Grid Rendering Networks for 3D Object Detection in Point Clouds
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