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In this paper, we introduce a Point Recurrent Neural Network (PointRNN) formoving point cloud processing. At each time step, PointRNN takes pointcoordinates $\boldsymbol{P} \in \mathbb{R}^{n \times 3}$ and point features$\boldsymbol{X} \in \mathbb{R}^{n \times d}$ as input ($n$ and $d$ denote thenumber of points and the number of feature channels, respectively). The stateof PointRNN is composed of point coordinates $\boldsymbol{P}$ and point states$\boldsymbol{S} \in \mathbb{R}^{n \times d'}$ ($d'$ denotes the number of statechannels). Similarly, the output of PointRNN is composed of $\boldsymbol{P}$and new point features $\boldsymbol{Y} \in \mathbb{R}^{n \times d''}$ ($d''$denotes the number of new feature channels). Since point clouds are orderless,point features and states from two time steps can not be directly operated.Therefore, a point-based spatiotemporally-local correlation is adopted toaggregate point features and states according to point coordinates. We furtherpropose two variants of PointRNN, i.e., Point Gated Recurrent Unit (PointGRU)and Point Long Short-Term Memory (PointLSTM). We apply PointRNN, PointGRU andPointLSTM to moving point cloud prediction, which aims to predict the futuretrajectories of points in a set given their history movements. Experimentalresults show that PointRNN, PointGRU and PointLSTM are able to produce correctpredictions on both synthetic and real-world datasets, demonstrating theirability to model point cloud sequences. The code has been released at\url{https://github.com/hehefan/PointRNN}.

PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing