4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks

• URL: http://arxiv.org/abs/2306.01081v1
• Date: Thu, 1 Jun 2023 18:43:16 GMT
• Title: 4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks
• Authors: Lorenzo Berlincioni, Stefano Berretti, Marco Bertini, Alberto Del Bimbo
• Abstract summary: We propose a new solution for upscaling and restoration of time-varying 3D video point clouds after they have been compressed. Our model consists of a specifically designed Graph Convolutional Network (GCN) that combines Dynamic Edge Convolution and Graph Attention Networks.
• Score: 29.615723135027096
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Time varying sequences of 3D point clouds, or 4D point clouds, are now being acquired at an increasing pace in several applications (e.g., LiDAR in autonomous or assisted driving). In many cases, such volume of data is transmitted, thus requiring that proper compression tools are applied to either reduce the resolution or the bandwidth. In this paper, we propose a new solution for upscaling and restoration of time-varying 3D video point clouds after they have been heavily compressed. In consideration of recent growing relevance of 3D applications, %We focused on a model allowing user-side upscaling and artifact removal for 3D video point clouds, a real-time stream of which would require . Our model consists of a specifically designed Graph Convolutional Network (GCN) that combines Dynamic Edge Convolution and Graph Attention Networks for feature aggregation in a Generative Adversarial setting. By taking inspiration PointNet++, We present a different way to sample dense point clouds with the intent to make these modules work in synergy to provide each node enough features about its neighbourhood in order to later on generate new vertices. Compared to other solutions in the literature that address the same task, our proposed model is capable of obtaining comparable results in terms of quality of the reconstruction, while using a substantially lower number of parameters (about 300KB), making our solution deployable in edge computing devices such as LiDAR.

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