Ground Truth Evaluation for Event-Based Silicon Retina Stereo Data

In this paper we present a new approach for the evaluation of event-based Silicon Retina stereo matching results. The evaluation of stereo matching algorithm results is a necessary task for the development, comparison, and improvement of depth generating camera systems. In contrast to conventional frame-based cameras, the silicon retina sensors delivers asynchronous events instead of synchronous intensity or color images. The polarity of the events represents either an increase (on-event) or a decrease (off-event) of the brightness of the projected scene point. This is the reason why existing ground truth data and evaluation platforms are not suitable for testing silicon retina stereo camera systems. For the analysis of the introduced novel evaluation method, we use an area-based (sum of absolute difference) algorithm for the event-driven sensor system. A conventional video camera stereo vision system is used to produce reference data. The results show that the presented method offers new opportunities for the evaluation of stereo matching results computed from silicon retina stereo data.

[1]  T. Delbruck,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < 1 , 2022 .

[2]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Markus Vincze,et al.  A fast stereo matching algorithm suitable for embedded real-time systems , 2010, Comput. Vis. Image Underst..

[4]  Richard Szeliski,et al.  High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[5]  Massimo A. Sivilotti,et al.  Wiring considerations in analog VLSI systems, with application to field-programmable networks , 1992 .

[6]  Misha Anne Mahowald,et al.  VLSI analogs of neuronal visual processing: a synthesis of form and function , 1992 .

[7]  Daniel Matolin,et al.  A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS , 2011, IEEE Journal of Solid-State Circuits.

[8]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Christoph Sulzbachner,et al.  A novel verification approach for silicon retina stereo matching algorithms , 2010, Proceedings ELMAR-2010.

[10]  Christoph Sulzbachner,et al.  Event-Based Stereo Matching Approaches for Frameless Address Event Stereo Data , 2011, ISVC.

[11]  Misha Mahowald,et al.  A silicon model of early visual processing , 1993, Neural Networks.

[12]  Tobi Delbrück,et al.  Improved ON/OFF temporally differentiating address-event imager , 2004, Proceedings of the 2004 11th IEEE International Conference on Electronics, Circuits and Systems, 2004. ICECS 2004..

[13]  T. Vaudrey,et al.  Differences between stereo and motion behaviour on synthetic and real-world stereo sequences , 2008, 2008 23rd International Conference Image and Vision Computing New Zealand.

[14]  Bernd Jähne,et al.  Outdoor stereo camera system for the generation of real-world benchmark data sets , 2012 .

[15]  Tobi Delbrück,et al.  Asynchronous Event-Based Binocular Stereo Matching , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[16]  C. Mead,et al.  Neuromorphic Robot Vision with Mixed Analog- Digital Architecture , 2005 .

[17]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[18]  Tobi Delbrück,et al.  A 128$\times$ 128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor , 2008, IEEE Journal of Solid-State Circuits.

[19]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[20]  Tobi Delbrück,et al.  A 128 X 128 120db 30mw asynchronous vision sensor that responds to relative intensity change , 2006, 2006 IEEE International Solid State Circuits Conference - Digest of Technical Papers.