Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of is), very high dynamic range (140dB vs. 60dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.

Event-based Vision: A Survey

We propose a method which can perform real-time 3D reconstruction from a single hand-held event camera with no additional sensing, and works in unstructured scenes of which it has no prior knowledge. It is based on three decoupled probabilistic filters, each estimating 6-DoF camera motion, scene logarithmic (log) intensity gradient and scene inverse depth relative to a keyframe, and we build a real-time graph of these to track and model over an extended local workspace. We also upgrade the gradient estimate for each keyframe into an intensity image, allowing us to recover a real-time video-like intensity sequence with spatial and temporal super-resolution from the low bit-rate input event stream. To the best of our knowledge, this is the first algorithm provably able to track a general 6D motion along with reconstruction of arbitrary structure including its intensity and the reconstruction of grayscale video that exclusively relies on event camera data.

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Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of μs), very high dynamic range (140 dB versus 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world. 

Event-Based Vision: A Survey

We consider the recovery of a (real- or complex-valued) signal from magnitude-only measurements, known as phase retrieval. We formulate phase retrieval as a convex optimization problem, which we call PhaseMax. Unlike other convex methods that use semidefinite relaxation and lift the phase retrieval problem to a higher dimension, PhaseMax is a “non-lifting” relaxation that operates in the original signal dimension. We show that the dual problem to PhaseMax is basis pursuit, which implies that the phase retrieval can be performed using algorithms initially designed for sparse signal recovery. We develop sharp lower bounds on the success probability of PhaseMax for a broad range of random measurement ensembles, and we analyze the impact of measurement noise on the solution accuracy. We use numerical results to demonstrate the accuracy of our recovery guarantees, and we showcase the efficacy and limits of PhaseMax in practice.

PhaseMax: Convex Phase Retrieval via Basis Pursuit

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the output is composed of a sequence of asynchronous events rather than actual intensity images, traditional vision algorithms cannot be applied, so that a paradigm shift is needed. We introduce the problem of event-based multi-view stereo (EMVS) for event cameras and propose a solution to it. Unlike traditional MVS methods, which address the problem of estimating dense 3D structure from a set of known viewpoints, EMVS estimates semi-dense 3D structure from an event camera with known trajectory. Our EMVS solution elegantly exploits two inherent properties of an event camera: (1) its ability to respond to scene edges—which naturally provide semi-dense geometric information without any pre-processing operation—and (2) the fact that it provides continuous measurements as the sensor moves. Despite its simplicity (it can be implemented in a few lines of code), our algorithm is able to produce accurate, semi-dense depth maps, without requiring any explicit data association or intensity estimation. We successfully validate our method on both synthetic and real data. Our method is computationally very efficient and runs in real-time on a CPU.

/pdf/emvs-event-based-multi-view-stereo-3d-reconstruction-with-an-9ojz7c9df1.pdf

EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time

Conventional binocular head-mounted displays (HMDs) vary the stimulus to vergence with the information in the picture, while the stimulus to accommodation remains fixed at the apparent distance of the display, as created by the viewing optics. Sustained vergence-accommodation conflict (VAC) has been associated with visual discomfort, motivating numerous proposals for delivering near-correct accommodation cues. We introduce focal surface displays to meet this challenge, augmenting conventional HMDs with a phase-only spatial light modulator (SLM) placed between the display screen and viewing optics. This SLM acts as a dynamic freeform lens, shaping synthesized focal surfaces to conform to the virtual scene geometry. We introduce a framework to decompose target focal stacks and depth maps into one or more pairs of piecewise smooth focal surfaces and underlying display images. We build on recent developments in "optimized blending" to implement a multifocal display that allows the accurate depiction of occluding, semi-transparent, and reflective objects. Practical benefits over prior accommodation-supporting HMDs are demonstrated using a binocular focal surface display employing a liquid crystal on silicon (LCOS) phase SLM and an organic light-emitting diode (OLED) display.

/pdf/focal-surface-displays-3nkf9s74pf.pdf

Focal surface displays

In this work, we propose using camera arrays coupled with coherent illumination as an effective method of improving spatial resolution in long distance images by a factor of ten and beyond. Recent advances in ptychography have demonstrated that one can image beyond the diffraction limit of the objective lens in a microscope. We demonstrate a similar imaging system to image beyond the diffraction limit in long range imaging. We emulate a camera array with a single camera attached to an $XY$ translation stage. We show that an appropriate phase retrieval based reconstruction algorithm can be used to effectively recover the lost high resolution details from the multiple low resolution acquired images. We analyze the effects of noise, required degree of image overlap, and the effect of increasing synthetic aperture size on the reconstructed image quality. We show that coherent camera arrays have the potential to greatly improve imaging performance. Our simulations show resolution gains of $10\times$ and more are achievable. Furthermore, experimental results from our proof-of-concept systems show resolution gains of $4\times - 7\times$ for real scenes. All experimental data and code is made publicly available on the project webpage. Finally, we introduce and analyze in simulation a new strategy to capture macroscopic Fourier Ptychography images in a single snapshot, albeit using a camera array.

/pdf/toward-long-distance-subdiffraction-imaging-using-coherent-2bnp0srrl7.pdf

Toward Long-Distance Subdiffraction Imaging Using Coherent Camera Arrays

We present a prototype compressive video camera that encodes scene movement using a translated binary photomask in the optical path. The encoded recording can then be used to reconstruct multiple output frames from each captured image, effectively synthesizing high speed video. The use of a printed binary mask allows reconstruction at higher spatial resolutions than has been previously demonstrated. In addition, we improve upon previous work by investigating tradeoffs in mask design and reconstruction algorithm selection. We identify a mask design that consistently provides the best performance across multiple reconstruction strategies in simulation, and verify it with our prototype hardware. Finally, we compare reconstruction algorithms and identify the best choice in terms of balancing reconstruction quality and speed.

High spatio-temporal resolution video with compressed sensing.

Structured light 3D scanning systems are fundamentally constrained by limited sensor bandwidth and light source power, hindering their performance in real-world applications where depth information is essential, such as industrial automation, autonomous transportation, robotic surgery, and entertainment. We present a novel structured light technique called Motion Contrast 3D scanning (MC3D) that maximizes bandwidth and light source power to avoid performance trade-offs. The technique utilizes motion contrast cameras that sense temporal gradients asynchronously, i.e., independently for each pixel, a property that minimizes redundant sampling. This allows laser scanning resolution with single-shot speed, even in the presence of strong ambient illumination, significant inter-reflections, and highly reflective surfaces. The proposed approach will allow 3D vision systems to be deployed in challenging and hitherto inaccessible real-world scenarios requiring high performance using limited power and bandwidth.

MC3D: Motion Contrast 3D Scanning

In this work, we propose using camera arrays coupled with coherent illumination as an effective method of improving spatial resolution in long distance images by a factor of ten and beyond. Recent advances in ptychography have demonstrated that one can image beyond the diffraction limit of the objective lens in a microscope. We demonstrate a similar imaging system to image beyond the diffraction limit in long range imaging. We emulate a camera array with a single camera attached to an X-Y translation stage. We show that an appropriate phase retrieval based reconstruction algorithm can be used to effectively recover the lost high resolution details from the multiple low resolution acquired images. We analyze the effects of noise, required degree of image overlap, and the effect of increasing synthetic aperture size on the reconstructed image quality. We show that coherent camera arrays have the potential to greatly improve imaging performance. Our simulations show resolution gains of 10x and more are achievable. Furthermore, experimental results from our proof-of-concept systems show resolution gains of 4x-7x for real scenes. Finally, we introduce and analyze in simulation a new strategy to capture macroscopic Fourier Ptychography images in a single snapshot, albeit using a camera array.

Nathan Matsuda

Papers

Focal surface displays

Toward Long-Distance Subdiffraction Imaging Using Coherent Camera Arrays

High spatio-temporal resolution video with compressed sensing.

MC3D: Motion Contrast 3D Scanning

Toward Long Distance, Sub-diffraction Imaging Using Coherent Camera Arrays