Abstract
Point clouds are an efficient data format for 3D data.
However, existing 3D segmentation methods for point
clouds either do not model local dependencies [21] or require added computations [14, 23]. This work presents a
novel 3D segmentation framework, RSNet1
, to efficiently
model local structures in point clouds. The key component of the RSNet is a lightweight local dependency module. It is a combination of a novel slice pooling layer, Recurrent Neural Network (RNN) layers, and a slice unpooling layer. The slice pooling layer is designed to project
features of unordered points onto an ordered sequence of
feature vectors so that traditional end-to-end learning algorithms (RNNs) can be applied. The performance of RSNet is
validated by comprehensive experiments on the S3DIS[1],
ScanNet[3], and ShapeNet [34] datasets. In its simplest
form, RSNets surpass all previous state-of-the-art methods
on these benchmarks. And comparisons against previous
state-of-the-art methods [21, 23] demonstrate the efficiency
of RSNets