Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging

Jayasuriya, Suren; Pediredla, Adithya; Sivaramakrishnan, Sriram; Molnar, Alyosha; Veeraraghavan, Ashok

doi:10.1109/3dv.2015.8

Cited by 15 publications

(7 citation statements)

References 41 publications

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“…Extracting the constituent phases and amplitudes is a complicated signal processing problem, requiring additional measurements. We can classify prior approaches in the vein of multifrequency phase ToF [30,22,13,3,4,6,31,5] or space-time light transport [33,40,41,29,39,18,10,28,19,27,21,31]. 1 The latter approach is complementary to this paper, as our approach is in the context of a single scene point and could be later extended with spatial analysis.…”

Section: Related Workmentioning

confidence: 99%

Macroscopic Interferometry: Rethinking Depth Estimation with Frequency-Domain Time-of-Flight

Kadambi

Schiel

Raskar

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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A form of meter-scale, macroscopic interferometry is proposed using conventional time-of-flight (ToF) sensors. Today, ToF sensors use phase-based sampling, where the phase delay between emitted and received, high-frequency signals encodes distance. This paper examines an alternative ToF architecture, inspired by micron-scale, microscopic interferometry, that relies only on frequency sampling: we refer to our proposed macroscopic technique as Frequency-Domain Time of Flight (FD-ToF). The proposed architecture offers several benefits over existing phase ToF systems, such as robustness to phase wrapping and implicit resolution of multi-path interference, all while capturing the same number of subframes. A prototype camera is constructed to demonstrate macroscopic interferometry at meter scale.

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Section: Related Workmentioning

confidence: 99%

Macroscopic Interferometry: Rethinking Depth Estimation with Frequency-Domain Time-of-Flight

Kadambi

Schiel

Raskar

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Traditional light-based sensing, such as cameras, face limitations in obtaining occlusion-related information due to the shorter wavelength of light compared to sound, preventing it from going behind an object. With the increasing need to improve the safety of autonomous vehicles and robots, recent engineering efforts have sought to overcome this limitation by acquiring occlusion information using cameras and lasers [4,[29][30][31][32]. Sound waves have longer wavelengths than light and can penetrate objects and reach behind them.…”

Section: Utilizing Information Obtained From Occlusion Spots In Batsmentioning

confidence: 99%

Discrimination of object information by bat echolocation deciphered from acoustic simulations

Teshima,

Mogi,

Nishida

et al. 2024

R. Soc. Open Sci.

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High-precision visual sensing has been achieved by combining cameras with deep learning. However, an unresolved challenge involves identifying information that remains elusive for optical sensors, such as occlusion spots hidden behind objects. Compared to light, sound waves have longer wavelengths and can, therefore, collect information on occlusion spots. In this study, we investigated whether bats could perform advanced sound sensing using echolocation to acquire a target's occlusion information. We conducted a two-alternative forced choice test on Pipistrellus abramus with five different targets, including targets with high visual similarity from the front, but different backend geometries, i.e. occlusion spots or textures. Subsequently, the echo impulse responses produced by these targets, which were difficult to obtain with real measurements, were computed using three-dimensional acoustic simulations to provide a detailed analysis consisting of the acoustic cues that the bats obtained through echolocation. Our findings demonstrated that bats could effectively discern differences in target occlusion spot structure and texture through echolocation. Furthermore, the discrimination performance was related to the differences in the logarithmic spectral distortion of the occlusion-related components in the simulated echo impulse responses. This suggested that the bats obtained occlusion information through echolocation, highlighting the advantages of utilizing broadband ultrasound for sensing.

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“…Ng et al [23] proposed a plenoptic camera that captures 4D light field with a single exposure, which is of compact structure, portable, and can also help refocusing. With CI, light field camera has also been employed to overcome the spatio-angular resolution trade-off [24], and extended to capture dynamic and transient scenes [25]- [27]. • Depth from focus / defocus estimates the 3D surface of a scene from a set of two or more images of that scene.…”

Section: A Depth Imagingmentioning

confidence: 99%

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Suo¹,

Zhang²,

Gong³

et al. 2021

Preprint

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Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision. Recent explosive progresses in Artificial Intelligence (AI) have shown great potential to develop advanced mobile platforms with new imaging devices. Traditional imaging systems based on the "capturing images first and processing afterwards" mechanism cannot meet this unprecedented demand. Differently, Computational Imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real systems by integrating deep learning algorithms into the mobile vision platform to achieve the closed loop of intelligent acquisition, processing and decision making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advances of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Motivated by the fact that most existing studies only loosely connect CI and AI (usually using AI to improve the performance of CI and only limited works have deeply connected them), in this work, we propose a framework to deeply integrate CI and AI by using the example of self-driving vehicles with high-speed communication, edge computing and traffic planning. Finally, we outlook the future of CI plus AI by investigating new materials, brain science and new computing techniques to shed light on new directions of mobile vision systems.

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Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging

Cited by 15 publications

References 41 publications

Macroscopic Interferometry: Rethinking Depth Estimation with Frequency-Domain Time-of-Flight

Macroscopic Interferometry: Rethinking Depth Estimation with Frequency-Domain Time-of-Flight

Discrimination of object information by bat echolocation deciphered from acoustic simulations

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

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