Browsing by Author "Schuster, Kersten"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Compression and Rendering of Textured Point Clouds via Sparse Coding
    (The Eurographics Association, 2021) Schuster, Kersten; Trettner, Philip; Schmitz, Patric; Schakib, Julian; Kobbelt, Leif; Binder, Nikolaus and Ritschel, Tobias
    Splat-based rendering techniques produce highly realistic renderings from 3D scan data without prior mesh generation. Mapping high-resolution photographs to the splat primitives enables detailed reproduction of surface appearance. However, in many cases these massive datasets do not fit into GPU memory. In this paper, we present a compression and rendering method that is designed for large textured point cloud datasets. Our goal is to achieve compression ratios that outperform generic texture compression algorithms, while still retaining the ability to efficiently render without prior decompression. To achieve this, we resample the input textures by projecting them onto the splats and create a fixed-size representation that can be approximated by a sparse dictionary coding scheme. Each splat has a variable number of codeword indices and associated weights, which define the final texture as a linear combination during rendering. For further reduction of the memory footprint, we compress geometric attributes by careful clustering and quantization of local neighborhoods. Our approach reduces the memory requirements of textured point clouds by one order of magnitude, while retaining the possibility to efficiently render the compressed data.
  • Thumbnail Image
    Item
    Interactive Segmentation of Textured Point Clouds
    (The Eurographics Association, 2022) Schmitz, Patric; Suder, Sebastian; Schuster, Kersten; Kobbelt, Leif; Bender, Jan; Botsch, Mario; Keim, Daniel A.
    We present a method for the interactive segmentation of textured 3D point clouds. The problem is formulated as a minimum graph cut on a k-nearest neighbor graph and leverages the rich information contained in high-resolution photographs as the discriminative feature. We demonstrate that the achievable segmentation accuracy is significantly improved compared to using an average color per point as in prior work. The method is designed to work efficiently on large datasets and yields results at interactive rates. This way, an interactive workflow can be realized in an immersive virtual environment, which supports the segmentation task by improved depth perception and the use of tracked 3D input devices. Our method enables to create high-quality segmentations of textured point clouds fast and conveniently.