2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-9.2
Paper Title ZERO IN ON SHAPE: A GENERIC 2D-3D INSTANCE SIMILARITY METRIC LEARNED FROM SYNTHETIC DATA
Authors Maciej Janik, Non-affiliated, Poland; Niklas Gard, Anna Hilsmann, Peter Eisert, Fraunhofer Heinrich-Hertz-Institute, Germany
SessionARS-9: Interpretation, Understanding, Retrieval
LocationArea I
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Interpretation and Understanding
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We present a network architecture which compares RGB images and untextured 3D models by the similarity of the represented shape. Our system is optimised for Zero-Shot retrieval, meaning it can recognise shapes never shown in training. We use a view-based shape descriptor and a Siamese Network to learn object geometry from pairs of 3D models and 2D images. Due to scarcity of datasets with exact photograph-mesh correspondences, we train our network with only synthetic data. Our experiments investigate the effect of different qualities and quantities of training data on retrieval accuracy and present insights from bridging the domain gap. We show that increasing the variety of synthetic data improves retrieval accuracy and that our system’s performance in zero-shot mode can match that of the instance-aware mode, as far as narrowing down the search to the top 10% of objects.