LAUSR

In recent years, there has been a rapid growth in applications that rely on point clouds to represent the 3D world, driven by the increasing demand for immersive and other related scenarios. However, compressing the large and high-precision point cloud data efficiently while maintaining high perceptual quality for human vision remains a challenge. To solve the problem, we propose a new structure-aware generative point cloud compression framework for human vision. In the encoder, we focus on information that is more sensitive to the human vision and obtain this type of information from different scale. This allows us to capture structural importance information from global scale and local scale, which are more difficult to reconstruct. For the decoder, we introduce a progressive generative reconstruction approach that utilizes acquired information from the encoder to guide the generation of point cloud surfaces. Moreover, we propose a novel probability cloud-based discriminator. Instead of directly assessing the authenticity of the generated point clouds, our discriminator evaluates the probability distribution of the existence of points within the generated point cloud. This approach reduces the difficulty of discrimination while effectively improving the accuracy of the generator in generating probability distributions. According to the correct probability, we can obtain a high accuracy point cloud by pruning the points with low probability. Through comprehensive experiments, we demonstrate the effectiveness and superiority of our proposed framework in terms of encoding efficiency, high perceptual quality, and generation quality.