论文标题
ipdae：改进基于补丁的深度自动编码器，用于有损点云几何压缩
IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression
论文作者
论文摘要
Point Cloud是3D内容的至关重要表示，在虚拟现实，混合现实，自主驾驶等许多领域已广泛使用，随着数据中点数的增加，如何有效地压缩点云成为一个具有挑战性的问题。在本文中，我们提出了一组基于贴片的点云压缩的重大改进，即用于熵编码的可学习上下文模型，用于采样中心点的OCTREE编码以及集成的压缩和训练过程。此外，我们提出了一个对抗网络，以改善重建过程中点的均匀性。我们的实验表明，改进的基于贴片的自动编码器在稀疏和大规模点云上的速率延伸性能方面的表现优于最先进的。更重要的是，我们的方法可以在确保重建质量的同时保持短时间的压缩时间。
Point cloud is a crucial representation of 3D contents, which has been widely used in many areas such as virtual reality, mixed reality, autonomous driving, etc. With the boost of the number of points in the data, how to efficiently compress point cloud becomes a challenging problem. In this paper, we propose a set of significant improvements to patch-based point cloud compression, i.e., a learnable context model for entropy coding, octree coding for sampling centroid points, and an integrated compression and training process. In addition, we propose an adversarial network to improve the uniformity of points during reconstruction. Our experiments show that the improved patch-based autoencoder outperforms the state-of-the-art in terms of rate-distortion performance, on both sparse and large-scale point clouds. More importantly, our method can maintain a short compression time while ensuring the reconstruction quality.
