Ground Truth Evaluation for Event-Based Silicon Retina Stereo Data

Kogler, Juergen; Eibensteiner, Florian; Humenberger, Martin; Gelautz, Margrit; Scharinger, Josef

doi:10.1109/cvprw.2013.98

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…For depth perception, the event-based method depends on the responses of modeling neurons and simulates the 3D reconstructions of objects [46]- [48]. This method uses the pair of retinal cameras to simulate binocular vision.…”

Section: Simulations Of Depth Perceptionmentioning

confidence: 99%

Depth Perception With Interocular Blur Differences Based on a Spiking Network

Liu

2022

IEEE Access

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Visual depth perception is the basic function of the visual nervous system. To a stimulus in the stereo space, the visual nervous system could generate a perception about depth of its position. Experimental data have demonstrated that interocular blur differences could lead to illusory perceptions about depths of moving stimuli. However, to stimuli with interocular blur differences, influences of different factors on illusory depth perceptions are still unclear. To explore these influences, this paper constructs a plastic two-layer k-winner-take-all (k-WTA) spiking network, simulating primary visual cortical responses. With incompatible stimuli presented into two eyes in experiments, binocular rivalry could occur in the primary visual cortex and interact with depth perception. To simulate binocular rivalry, the network consists of two parallel visual channels driven by left-eye and right-eye stimuli and competing with each other through mutual inhibition. In simulations, the horizontally moving stimulus is filtered with different Gaussian filters to generate paired monocular stimuli with interocular blur differences. The blurry strength, the moving direction and the moving speed perform as varying factors of moving stimuli. The network updates its dynamics through probabilistic inference, reflecting impacts of each factor on both neural responses and binocular rivalry. The modified responses could simulate illusory depth perceptions of stimuli as observed in experiments. To stimuli with interocular blur differences, varying factors could modify binocular rivalry in the network, inducing distinguishing illusory depth perceptions. Based on probabilistic inference, our model could provide possible explanations to illusory depth perceptions with interocular blur differences.INDEX TERMS Depth perception, Binocular rivalry, Winner-take-all, Spiking neural networks.

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Section: Simulations Of Depth Perceptionmentioning

confidence: 99%

Depth Perception With Interocular Blur Differences Based on a Spiking Network

Liu

2022

IEEE Access

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“…For the latter we use a different stereo sensor consisting of monochrome cameras which achieves an accuracy of 3% at a distance of 3.4m. More details about the ground truth generation and the registration of the depth results from both stereo sensors onto each other are presented in the work of Kogler et al [10]. Figure 6 shows the average distance error achieved by the event-based stereo vision algorithm (triangles) compared to an area-based SAD algorithm (squares) which is explained in [12].…”

Section: Resultsmentioning

confidence: 99%

A High-Performance Hardware Architecture for a Frameless Stereo Vision Algorithm Implemented on a FPGA Platform

Eibensteiner

Kogler

Scharinger

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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As a novelty, in this paper we present an event-based stereo vision matching approach based on time-correlation using segmentation to restrict the matching process to active image areas, exploiting the event-driven behavior of a silicon retina sensor. Stereo matching is used in depth generating camera systems for solving the correspondence problem and reconstructing 3D data. Using conventionally frame-based cameras, this correspondence problem is a time consuming and computationally expensive task. To overcome this issue, embedded systems can be used to speed up the calculation of stereo matching results. The silicon retina delivers asynchronous events if the illumination changes instead of synchronous intensity or color images. It provides sparse input data and therefore the output of the stereo vision algorithm (depth map) is also sparse. The high temporal resolution of such event-driven sensors leads to high data rates. To handle these and the correspondence problem in real time, we implemented our stereo matching algorithm for a field programmable gate array (FPGA). The results show that our matching criterion, based on the time of occurrence of an event, leads to a small average distance error and the parallel hardware architecture and efficient memory utilization results in a frame rate of up to 1140fps.

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“…However, the ground truth is estimated by manually measuring the distance between the camera and object and assumed all the triggered events are in the same disparity. Recently, some datasets for event-based simultaneous localization and mapping (SLAM) (Kogler et al, 2013 ; Serrano-Gotarredona et al, 2013 ) have become available, but none of those are created for event based stereo matching and the above previous works do not release their test datasets.…”

Section: Methodsmentioning

confidence: 99%

Event-Based Stereo Depth Estimation Using Belief Propagation

2017

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Compared to standard frame-based cameras, biologically-inspired event-based sensors capture visual information with low latency and minimal redundancy. These event-based sensors are also far less prone to motion blur than traditional cameras, and still operate effectively in high dynamic range scenes. However, classical framed-based algorithms are not typically suitable for these event-based data and new processing algorithms are required. This paper focuses on the problem of depth estimation from a stereo pair of event-based sensors. A fully event-based stereo depth estimation algorithm which relies on message passing is proposed. The algorithm not only considers the properties of a single event but also uses a Markov Random Field (MRF) to consider the constraints between the nearby events, such as disparity uniqueness and depth continuity. The method is tested on five different scenes and compared to other state-of-art event-based stereo matching methods. The results show that the method detects more stereo matches than other methods, with each match having a higher accuracy. The method can operate in an event-driven manner where depths are reported for individual events as they are received, or the network can be queried at any time to generate a sparse depth frame which represents the current state of the network.

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Ground Truth Evaluation for Event-Based Silicon Retina Stereo Data

Cited by 10 publications

References 15 publications

Depth Perception With Interocular Blur Differences Based on a Spiking Network

Depth Perception With Interocular Blur Differences Based on a Spiking Network

A High-Performance Hardware Architecture for a Frameless Stereo Vision Algorithm Implemented on a FPGA Platform

Event-Based Stereo Depth Estimation Using Belief Propagation

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