A geometry-aware compression of 3D mesh texture with random access

Abstract

A 3D mesh object is usually represented as a combination of several entities including geometrical information (i.e., the triangles and their position in space) and a texture atlas/map (i.e. a giant 2D image containing all the texture information that is mapped to the 3D object at the rendering stage). This atlas is usually compressed using a conventional 2D image coder, thus without taking into account the geometrical information. Moreover, the whole image is usually decoded even though only a subpart of the mesh is observed by a user. In this paper, we propose a novel approach to compress a texture atlas of a 3D model that enables random access during decoding, and nevertheless takes into account the correlation driven by the geometrical information. The experimental results demonstrate the benefits of the proposed coder.

Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering

“…However, at the current development phase, the large-sized meshes cannot be rendered from their compressed mesh texture. A contribution worth mentioning on texture atlas compression preserves the geometric attributes of the 3D model with random access decoding and view-dependent transmission [47]. Finally, some fixed functions of OpenGL and principal methods for performance boost and visual quality include frustrum culling, per-vertex operations, shader reduction and texture filtering.…”

Excess rate for model selection in interactive compression using belief propagation decoding

“…2.1 IC and excess rate for model selection IC refers to the problem of compressing data, while allowing the user to access any part of the data in the compressed domain. Interactivity with a visual content occurs with several image modalities such as omnidirectional images [3], or texture maps of 3D models [10]. Common to both applications is that the image to be compressed is split into small blocks where the blocks are encoded/decoded one after the other.…”

“…Similar to SW coding, excess rate in IC remains an open and difficult problem. To overcome this issue and solve the model selection problem (2), we propose a closed formula for the excess rate in IC coding in one case of practical interest [3,10], namely linear codes and BP decoding. For the sake of clarity, we denote ∆R IC BP (P XY k , Q X|Y k Q Y k ) the excess rate, when BP decoding is applied.…”