Temporal Consistency-Aware Dynamic Point Clouds Color Attribute Enhancement

Dynamic point clouds, widely used in virtual reality and autonomous driving systems, often suffer from distortions due to quantization in the process of compression. These distortions significantly degrade the visual quality of dynamic point clouds, especially temporal inconsistency. To address this issue, a temporal consistency-aware dynamic point clouds color attribute enhancement method is proposed in this work. Specifically, a 3D Spatial-Temporal Search (STS) module is designed to adaptively search point cloud patches in the temporal domain for feature alignment. These matched patches are then individually fed into Single Frame Feature Extraction (SFFE) module that comprises of multi-head attention and graph convolution to exploit latent features of point cloud color attribute. In addition, to further capture both the spatial and temporal dependencies, a Convolutional Point cloud Long Short-Term Memory (Conv-PointLSTM) network is applied, which integrates convolution and max pooling with LSTM mechanism to facilitate the color attribute correspondents across the spatial-temporal latent features. Experimental results demonstrate that the proposed method can achieve 0.44 dB gains on average in terms of Peak Signal-to-Noise Ratio (PSNR) and 1.50%/5.31%bit rate reductions at the low/high bit rate, which outperforms the state-of-the-art works. The source code and trained models are available athttps://github.com/xu-coder-666/DPC.