Dynamic point cloud compression using slicing focusing on self-occluded points

Abstract

Realistic digital representations of 3D objects and surroundings have been recently made possible. This is due to recent advances in computer graphics allowing real-time and realistic physical world interactions with users [1], [2]. Emerging technologies enable real-world objects, persons, and scenes to move dynamically across users' views convincingly using a 3D point cloud [3]–[5]. A point cloud is a set of individual 3D points that are not organized and without any relationship in the 3D space [1], [6]. Each point has a 3D position but can also contain some other attributes (e.g., texture, reflectance, colour, and normal), creating a realistic visual representation model for static and dynamic 3D objects [3], [7]. This is desirable for many applications such as geographic information systems, cultural heritage, immersive telepresence, telehealth, disabled access, 3D telepresence, telecommunication, autonomous driving, gaming and robotics, virtual reality (VR), and augmented reality (AR) [2], [8]. Even the use of point cloud in Metaverse when creating an avatar or content in Metaverse and object-based interaction is required. The Metaverse is a virtual world that creates a network where anyone can interact through their avatars [9]. Therefore, it is critical to present the 3D virtual world as close to the real world as possible, with high-resolution and minimal noise and blur.