PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing
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Point cloud is attracting more and more attention in the community. However, few works study dynamic point clouds. In this paper, we introduce a Point Recurrent Neural Network (PointRNN) unit for moving point cloud processing. To keep the spatial structure, rather than taking a sole one-dimensional vector x∈R^d like RNN as input, PointRNN takes points' coordinates P∈R^n × 3 and their features X∈R^n × d as inputs (n and d denote the number of points and feature dimensions, respectively). Accordingly, the state s∈R^d' in RNN are extended to (P, S∈R^n × d') in PointRNN (d' denotes the number of state dimensions). Since point clouds are orderless, features and states of two adjacent time steps can not be directly operated. Therefore, PointRNN replaces the concatenation operation in RNN with a correlation operation, which aggregates inputs and states according to points' coordinates. To evaluate PointRNN, we apply one of its variants, i.e., Point Long Short-Term Memory (PointLSTM), to moving point cloud prediction, which aims to predict the future trajectories of points in a cloud given their history movements. Experimental results show that PointLSTM is able to produce correct predictions on both synthetic and real-world datasets, demonstrating its effectiveness to model point cloud sequences. The code has been released at https://github.com/hehefan/PointRNN.
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