PG2021 Short Papers, Posters, and Work-in-Progress Papers

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2633094

Browse

Browsing PG2021 Short Papers, Posters, and Work-in-Progress Papers by Subject "Information systems"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Hierarchical Link and Code: Efficient Similarity Search for Billion-Scale Image Sets
    (The Eurographics Association, 2021) Yang, Kaixiang; Wang, Hongya; Du, Ming; Wang, Zhizheng; Tan, Zongyuan; Xiao, Yingyuan; Lee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard
    Similarity search is an indispensable component in many computer vision applications. To index billions of images on a single commodity server, Douze et al. introduced L&C that works on operating points considering 64-128 bytes per vector. While the idea is inspiring, we observe that L&C still suffers the accuracy saturation problem, which it is aimed to solve. To this end, we propose a simple yet effective two-layer graph index structure, together with dual residual encoding, to attain higher accuracy. Particularly, we partition vectors into multiple clusters and build the top-layer graph using the corresponding centroids. For each cluster, a subgraph is created with compact codes of the first-level vector residuals. Such an index structure provides better graph search precision as well as saves quite a few bytes for compression. We employ the second-level residual quantization to re-rank the candidates obtained through graph traversal, which is more efficient than regression-from-neighbors adopted by L&C. Comprehensive experiments show that our proposal obtains over 30% higher recall@1 than the state-of-thearts, and achieves up to 7.7x and 6.1x speedup over L&C on Deep1B and Sift1B, respectively.
  • Thumbnail Image
    Item
    Volumetric Video Streaming Data Reduction Method Using Front-mesh 3D Data
    (The Eurographics Association, 2021) Zhao, Xiaotian; Okuyama, Takafumi; Lee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard
    Volumetric video contents are attracting much attention across various industries for their six-degrees-of-freedom (6DoF) viewing experience. However, in terms of streaming, volumetric video contents still present challenges such as high data volume and bandwidth consumption, which results in high stress on the network. To solve this issue, we propose a method using frontmesh 3D data to reduce the data size without affecting the visual quality much from a user's perspective. The proposed method also reduces decoding and import time on the client side, which enables faster playback of 3D data. We evaluated our method in terms of data reduction and computation complexity and conducted a qualitative analysis by comparing rendering results with reference data at different diagonal angles. Our method successfully reduces data volume and computation complexity with minimal visual quality loss.