  We present an improved approach for 3D object detection in point cloud data
based on the Frustum PointNet (F-PointNet). Compared to the original
F-PointNet, our newly proposed method considers the point neighborhood when
computing point features. The newly introduced local neighborhood embedding
operation mimics the convolutional operations in 2D neural networks. Thus
features of each point are not only computed with the features of its own or of
the whole point cloud but also computed especially with respect to the features
of its neighbors. Experiments show that our proposed method achieves better
performance than the F-Pointnet baseline on 3D object detection tasks.
