Point clouds are becoming essential in key applications with advances in capture
technologies leading to large volumes of data. Compression is thus essential for storage …

Recent advancements in self-driving cars, robotics, and remote sensing have widened the
range of applications for 3D Point Cloud (PC) data. This data format poses several new …

Neural approximations of scalar-and vector fields, such as signed distance functions and
radiance fields, have emerged as accurate, high-quality representations. State-of-the-art …

In this paper, we propose a two-stage deep learning framework called VoxelContext-Net for
both static and dynamic point cloud compression. Taking advantages of both octree based …

Recent years have witnessed the growth of point cloud based applications for both
immersive media as well as 3D sensing for auto-driving, because of its realistic and fine …

Point clouds are a very rich 3D visual representation model, which has become increasingly
appealing for multimedia applications with immersion, interaction and realism requirements …

Point clouds have been recognized as a crucial data structure for 3D content and are
essential in a number of applications such as virtual and mixed reality, autonomous driving …

摘要随着传感器技术的不断发展, 三维点云被广泛应用于自动驾驶, 机器人, 遥感, 文物修复,
增强现实, 虚拟现实等领域的视觉任务中. 然而, 直接应用收集到的海量原始点云数据得到的效果 …

With the proliferation of sensor devices, the extensive utilization of three-dimensional data in
multimedia continues to grow. Point clouds are widely adopted within this domain because …

We present a novel compression algorithm for reducing the storage of LiDAR sensory data
streams. Our model exploits spatio-temporal relationships across multiple LIDAR sweeps to …