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Evaluation of 3D interest point detection techniques via human-generated ground truth

Published: 01 September 2012 Publication History

Abstract

In this paper, we present an evaluation strategy based on human-generated ground truth to measure the performance of 3D interest point detection techniques. We provide quantitative evaluation measures that relate automatically detected interest points to human-marked points, which were collected through a web-based application. We give visual demonstrations and a discussion on the results of the subjective experiments. We use a voting-based method to construct ground truth for 3D models and propose three evaluation measures, namely False Positive and False Negative Errors, and Weighted Miss Error to compare interest point detection algorithms.

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  • (2024)Towards 3D Colored Mesh Saliency: Database and BenchmarksIEEE Transactions on Multimedia10.1109/TMM.2023.331292426(3580-3591)Online publication date: 1-Jan-2024
  • (2023)Context-Aware 3D Points of Interest Detection via Spatial Attention MechanismACM Transactions on Multimedia Computing, Communications, and Applications10.1145/359702619:6(1-19)Online publication date: 12-Jul-2023
  • (2022)Detecting 3D Points of Interest Using Projective Neural NetworksIEEE Transactions on Multimedia10.1109/TMM.2021.307097724(1637-1650)Online publication date: 1-Jan-2022
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Information & Contributors

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Published In

cover image The Visual Computer: International Journal of Computer Graphics
The Visual Computer: International Journal of Computer Graphics  Volume 28, Issue 9
3DOR 2011
September 2012
55 pages

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 September 2012

Author Tags

  1. 3D salient points
  2. 3D shape analysis

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Cited By

View all
  • (2024)Towards 3D Colored Mesh Saliency: Database and BenchmarksIEEE Transactions on Multimedia10.1109/TMM.2023.331292426(3580-3591)Online publication date: 1-Jan-2024
  • (2023)Context-Aware 3D Points of Interest Detection via Spatial Attention MechanismACM Transactions on Multimedia Computing, Communications, and Applications10.1145/359702619:6(1-19)Online publication date: 12-Jul-2023
  • (2022)Detecting 3D Points of Interest Using Projective Neural NetworksIEEE Transactions on Multimedia10.1109/TMM.2021.307097724(1637-1650)Online publication date: 1-Jan-2022
  • (2022)Unsupervised Multi-View CNN for Salient View Selection and 3D Interest Point DetectionInternational Journal of Computer Vision10.1007/s11263-022-01592-x130:5(1210-1227)Online publication date: 1-May-2022
  • (2022)Attention-embedding mesh saliencyThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-022-02444-y39:5(1783-1795)Online publication date: 11-May-2022
  • (2022)3D interest point detection using balance-distortion oriented selectionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02371-439:2(733-747)Online publication date: 10-Jan-2022
  • (2021)Mesh Saliency via Weakly Supervised Classification-for-Saliency CNNIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.292879427:1(151-164)Online publication date: 1-Jan-2021
  • (2020)Skeleton-bridged Mesh Saliency and Mesh SimplificationProceedings of the 2020 4th International Conference on Computer Science and Artificial Intelligence10.1145/3445815.3445861(278-283)Online publication date: 11-Dec-2020
  • (2020)Unsupervised Detection of Distinctive Regions on 3D ShapesACM Transactions on Graphics10.1145/336678539:5(1-14)Online publication date: 31-May-2020
  • (2020)Multiscale bilateral filtering to detect 3D interest pointsIET Computer Vision10.1049/iet-cvi.2018.540514:1(36-47)Online publication date: 20-Jan-2020
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