The POP Buffer: Rapid Progressive Clustering by Geometry Quantization

Abstract

Within this paper, we present a novel, straightforward progressive encoding scheme for general triangle soups, which is particularly well-suited for mobile and Web-based environments due to its minimal requirements on the client's hardware and software. Our rapid encoding method uses a hierarchy of quantization to effectively reorder the original primitive data into several nested levels of detail. The resulting stateless buffer can progressively be transferred as-is to the GPU, where clustering is efficiently performed in parallel during rendering. We combine our approach with a crack-free mesh partitioning scheme to obtain a straightforward method for fast streaming and basic view-dependent LOD control.

Scalable Interactive Visualization for Connectomics

“…Progressive rendering provides such a solution through multiple meshes of varied levels of detail. 3DXP applies the existing format of POP Geometry [60] to direct bandwidth to the neural projections closest to the interactive camera.…”

Web-Based Scientific Exploration and Analysis of 3D Scanned Cuneiform Datasets for Collaborative Research

“…This results in a comparatively high compression combined with very low decompression times when compared to X3D and OpenCTM [16]. X3DDOM was further extended by the POP Buffer format [17] that employs a hierarchical quantization-based compression scheme, which enables progressive model loading. The Nexus format [18,19], which will be detailed in Section 3.2, introduces a hierarchical mesh reduction with fast decoding and view dependent rendering, enabling a progressive visualization of very large models.…”

Optimization Techniques for 3D Graphics Deployment on Mobile Devices

“…The optimization methods in this phase can be categorized into the following approaches: (1) remote caching, where all previously simplified 3D graphics are stored in a remote cache and re-used if similar requests appear [38], (2) traffic shaping, as combining multiple downloadable items into a single bundle will enable sending of larger data blocks instead of smaller ones hence favoring the wireless transmission channel [39], and (3) progressive downloading, in which a low quality version of the 3D graphics are first sent followed by incremental data that increases the visual quality of the 3D graphics [40].…”

“…As earlier mentioned, the capabilities of mobile hardware for 3D graphics re-production grow On-device optimization [8] x [33] x x [34] x [35] x [36] x [37] x [38] x x [39] x [40] x x [41] x [42] x [43] x x [44] x [53] x [54] x x [55] x x [56] x x [57] x x [58] x [60] x [61] x [62] x x [63] x x [64] x x [65] x [66] x [67] x [68] x [69] x [70] x [71] x [72] x [73] x [74] x x [76] x x [77] x [78] x [79] x [80] x [81] x [82] x [83] x [84] x extremely fast. For instance, 3D rendering performance of Tegra4 is roughly three-four times higher when compared to the previous generation [45].…”

“…For simplifying geometry, the literature introduces various methods that focus on simplifying different features of geometry. These methods include, but are not limited to, edge collapse [33,61], geometry quantization [40,62,63], geometry decimation [64], triangle decimation and cleansing conquest [40,62,65,66], mesh partitioning [56] and vertex clustering [54]. It should be noted that this article does not focus on the algorithmic details of each individual method, since this information is already comprehensively summarized, for instance, in recent surveys [60,67].…”