Josef Stumpfegger

Simon Niedermayr, Josef Stumpfegger, Rüdiger Westermann

Recently, high-fidelity scene reconstruction with an optimized 3D Gaussian splat representation has been introduced for novel view synthesis from sparse image sets. Making such representations suitable for applications like network streaming and rendering on low-power devices requires significantly reduced memory consumption as well as improved rendering efficiency. We propose a compressed 3D Gaussian splat representation that utilizes sensitivity-aware vector clustering with quantization-aware training to compress directional colors and Gaussian parameters. The learned codebooks have low bitrates and achieve a compression rate of up to $31\times$ on real-world scenes with only minimal degradation of visual quality. We demonstrate that the compressed splat representation can be efficiently rendered with hardware rasterization on lightweight GPUs at up to $4\times$ higher framerates than reported via an optimized GPU compute pipeline. Extensive experiments across multiple datasets demonstrate the robustness and rendering speed of the proposed approach.

Alexander Kumpf, Josef Stumpfegger, Patrick Fabian Härtl et al.

For an ensemble of data points in a multi-parameter space, we present a visual analytics technique to select a representative distribution of parameter values, and analyse how representative this distribution is in all ensemble members. A multi-parameter cluster in a representative ensemble member is visualized via a parallel coordinates plot, to provide initial distributions and let domain experts interactively select relevant parameters and value ranges. Since unions of value ranges select hyper-cubes in parameter space, data points in these unions are not necessarily contained in the cluster. By using a multi-parameter kD-tree to further refine the selected parameter ranges, in combination with a covariance analysis of refined sets of data points, a tight partition in multi-parameter space with reduced number of falsely selected points is obtained. To assess the representativeness of the selected multi-parameter distribution across the ensemble, a linked side-by-side view of per-member violin plots is provided. We propose modifications of violin plots to show multi-parameter distributions simultaneously, and investigate the visual design that effectively conveys (dis-)similarities in multi-parameter distributions. In a linked spatial view, users can analyse and compare the spatial distribution of selected points in different ensemble members via interval-based isosurface raycasting. In two real-world application cases we show how our approach is used to analyse the multi-parameter distributions across an ensemble of 3D fields.