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

A variety of techniques such as light field, structured illumination, and time-of-flight (TOF) are commonly used for depth acquisition in consumer imaging, robotics and many other applications. Unfortunately, each technique suffers from its individual limitations preventing robust depth sensing. In this paper, we explore the strengths and weaknesses of combining light field and time-of-flight imaging, particularly the feasibility of an on-chip implementation as a single hybrid depth sensor. We refer to this combination as depth field imaging. Depth fields combine light field advantages such as synthetic aperture refocusing with TOF imaging advantages such as high depth resolution and coded signal processing to resolve multipath interference. We show applications including synthesizing virtual apertures for TOF imaging, improved depth mapping through partial and scattering occluders, and single frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding, depth fields can improve depth sensing in the wild and generate new insights into the dimensions of light's plenoptic function.Comment: 9 pages, 8 figures, Accepted to 3DV 201

DeepToF: Off-the-shelf real-time correction of multipath interference in time-of-flight imaging

Time-of-flight (ToF) imaging has become a widespread technique for depth estimation, allowing affordable off-the-shelf cameras to provide depth maps in real time. However, multipath interference (MPI) resulting from indirect illumination significantly degrades the captured depth. Most previous works have tried to solve this problem by means of complex hardware modifications or costly computations. In this work, we avoid these approaches and propose a new technique to correct errors in depth caused by MPI, which requires no camera modifications and takes just 10 milliseconds per frame. Our observations about the nature of MPI suggest that most of its information is available in image space; this allows us to formulate the depth imaging process as a spatially-varying convolution and use a convolutional neural network to correct MPI errors. Since the input and output data present similar structure, we base our network on an autoencoder, which we train in two stages. First, we use the encoder (convolution filters) to learn a suitable basis to represent MPI-corrupted depth images; then, we train the decoder (deconvolution filters) to correct depth from synthetic scenes, generated by using a physically-based, time-resolved renderer. This approach allows us to tackle a key problem in ToF, the lack of ground-truth data, by using a large-scale captured training set with MPI-corrupted depth to train the encoder, and a smaller synthetic training set with ground truth depth to train the decoder stage of the network. We demonstrate and validate our method on both synthetic and real complex scenarios, using an off-the-shelf ToF camera, and with only the captured, incorrect depth as input

Recent advances in transient imaging: A computer graphics and vision perspective

Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation

Recent advances in transient imaging: A computer graphics and vision perspective

Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation

Phasor Imaging: A Generalization of Correlation-Based Time-of-Flight Imaging

In correlation-based time-of-flight (C-ToF) imaging systems, light sources with temporally varying intensities illuminate the scene. Due to global illumination, the temporally varying radiance received at the sensor is a combination of light received along multiple paths. Recovering scene properties (e.g., scene depths) from the received radiance requires separating these contributions, which is challenging due to the complexity of global illumination and the additional temporal dimension of the radiance. We propose phasor imaging, a framework for performing fast inverse light transport analysis using C-ToF sensors. Phasor imaging is based on the idea that by representing light transport quantities as phasors and light transport events as phasor transformations, light transport analysis can be simplified in the temporal frequency domain. We study the effect of temporal illumination frequencies on light transport, and show that for a broad range of scenes, global radiance (multi-path interference) vanishes for frequencies higher than a scene-dependent threshold. We use this observation for developing two novel scene recovery techniques. First, we present Micro ToF imaging, a ToF based shape recovery technique that is robust to errors due to global illumination. Second, we present a technique for separating the direct and global components of radiance. Both techniques require capturing as few as 3−4 images and minimal computations. We demonstrate the validity of the presented techniques via simulations and experiments performed with our hardware prototype

Efficient Methods for Computational Light Transport

En esta tesis presentamos contribuciones sobre distintos retos computacionales relacionados con transporte de luz. Los algoritmos que utilizan información sobre el transporte de luz están presentes en muchas aplicaciones de hoy en día, desde la generación de efectos visuales, a la detección de objetos en tiempo real. La luz es una valiosa fuente de información que nos permite entender y representar nuestro entorno, pero obtener y procesar esta información presenta muchos desafíos debido a la complejidad de las interacciones entre la luz y la materia. Esta tesis aporta contribuciones en este tema desde dos puntos de vista diferentes: algoritmos en estado estacionario, en los que se asume que la velocidad de la luz es infinita; y algoritmos en estado transitorio, que tratan la luz no solo en el dominio espacial, sino también en el temporal. Nuestras contribuciones en algoritmos estacionarios abordan problemas tanto en renderizado offline como en tiempo real. Nos enfocamos en la reducción de varianza para métodos offline,proponiendo un nuevo método para renderizado eficiente de medios participativos. En renderizado en tiempo real, abordamos las limitacionesde consumo de batería en dispositivos móviles proponiendo un sistema de renderizado que incrementa la eficiencia energética en aplicaciones gráficas en tiempo real. En el transporte de luz transitorio, formalizamos la simulación de este tipo transporte en este nuevo dominio, y presentamos nuevos algoritmos y métodos para muestreo eficiente para render transitorio. Finalmente, demostramos la utilidad de generar datos en este dominio, presentando un nuevo método para corregir interferencia multi-caminos en camaras Timeof- Flight, un problema patológico en el procesamiento de imágenes transitorias.n this thesis we present contributions to different challenges of computational light transport. Light transport algorithms are present in many modern applications, from image generation for visual effects to real-time object detection. Light is a rich source of information that allows us to understand and represent our surroundings, but obtaining and processing this information presents many challenges due to its complex interactions with matter. This thesis provides advances in this subject from two different perspectives: steady-state algorithms, where the speed of light is assumed infinite, and transient-state algorithms, which deal with light as it travels not only through space but also time. Our steady-state contributions address problems in both offline and real-time rendering. We target variance reduction in offline rendering by proposing a new efficient method for participating media rendering. In real-time rendering, we target energy constraints of mobile devices by proposing a power-efficient rendering framework for real-time graphics applications. In transient-state we first formalize light transport simulation under this domain, and present new efficient sampling methods and algorithms for transient rendering. We finally demonstrate the potential of simulated data to correct multipath interference in Time-of-Flight cameras, one of the pathological problems in transient imaging.<br /