CHuMI viewer: Compressive huge mesh interactive viewer

Abstract

The preprocessing of large meshes to provide and optimize interactive visualization implies a complete reorganization that often introduces significant data growth. This is detrimental to storage and network transmission, but in the near future could also affect the efficiency of the visualization process itself, because of the increasing gap between computing times and external access times. In this article, we attempt to reconcile lossless compression and visualization by proposing a data structure that radically reduces the size of the object while supporting a fast interactive navigation based on a viewing distance criterion. In addition to this double capability, this method works out-of-core and can handle meshes containing several hundred million vertices. Furthermore, it presents the advantage of dealing with any n-dimensional simplicial complex, including triangle soups or volumetric meshes, and provides a significant rate-distortion improvement. The performance attained is near state-of-the-art in terms of the compression ratio as well as the visualization frame rates, offering a unique combination that can be useful in numerous applications.

Optimization techniques of static 3D triangular mesh compression: A survey

“…This algorithm not only achieves such performance in rate-distortion to the reviewed in-core encoders, but also outperforms the single-rate OoC encoders in [36], [37] and [39]. Both Jamin et al [40] and Du et al [41] proposed a random accessible algorithm for encoding out-of-core multiresolution triangular meshes based on Kd-tree coder. In [40], the approach defined as CHuMI VIewer, compresses each node of a hierarchical structure (nSP-tree) independently of its vicinity.…”

“…Both Jamin et al [40] and Du et al [41] proposed a random accessible algorithm for encoding out-of-core multiresolution triangular meshes based on Kd-tree coder. In [40], the approach defined as CHuMI VIewer, compresses each node of a hierarchical structure (nSP-tree) independently of its vicinity. During this treatment, the boundaries are duplicated leading to compression ratio overhead.…”

3D Mesh Compression and Transmission for Mobile Robotic Applications

“…Assume V u = 15, since the relation between v and LFE becomes unstable and cannot be formulated when v > 15, according to our experiments. The BFS operation is repeated to pick a new set of anchors by reducing the threshold by 1, i.e., V u = 14, storing all newly selected anchors to the container Δ 14 . This process is repeated until…”

“…Relationship between the mesh quantization and the VDE (S q ). by 1, i.e., V u = 14, storing all newly selected anchors to the container 14 . This process is repeated until V u = V d , which is the lower bound threshold.…”

Simplification and streaming of GIS terrain for web clients

“…1. When we compare these results with the state of the art, there is still place for improvement: the best mesh compression methods obtain around 15 bits per vertex including the geometry information, as showed for instance in the comparative results of [Jamin et al 2009]. Nevertheless, those methods address only compression: using a visualization algorithm based on triangle strips is far less efficient regarding the execution times and do not provide the multiresolution.…”