Reconstruction of hidden 3D shapes using diffuse reflections

We analyze multi-bounce propagation of light in an unknown hidden volume and demonstrate that the reflected light contains sufficient information to recover the 3D structure of the hidden scene. We formulate the forward and inverse theory of secondary and tertiary scattering reflection using ideas from energy front propagation and tomography. We show that using careful choice of approximations, such as Fresnel approximation, greatly simplifies this problem and the inversion can be achieved via a backpropagation process. We provide a theoretical analysis of the invertibility, uniqueness and choices of space-time-angle dimensions using synthetic examples. We show that a 2D streak camera can be used to discover and reconstruct hidden geometry. Using a 1D high speed time of flight camera, we show that our method can be used recover 3D shapes of objects "around the corner"

Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging

The recovery of objects obscured by scattering is an important goal in imaging and has been approached by exploiting, for example, coherence properties, ballistic photons or penetrating wavelengths. Common methods use scattered light transmitted through an occluding material, although these fail if the occluder is opaque. Light is scattered not only by transmission through objects, but also by multiple reflection from diffuse surfaces in a scene. This reflected light contains information about the scene that becomes mixed by the diffuse reflections before reaching the image sensor. This mixing is difficult to decode using traditional cameras. Here we report the combination of a time-of-flight technique and computational reconstruction algorithms to untangle image information mixed by diffuse reflection. We demonstrate a three-dimensional range camera able to look around a corner using diffusely reflected light that achieves sub-millimetre depth precision and centimetre lateral precision over 40 cm×40 cm×40 cm of hidden space.MIT Media Lab ConsortiumUnited States. Defense Advanced Research Projects Agency. Young Faculty AwardMassachusetts Institute of Technology. Institute for Soldier Nanotechnologies (Contract W911NF-07-D-0004

Automatic Dense 3D Scene Mapping from Non-overlapping Passive Visual Sensors for Future Autonomous Systems

The ever increasing demand for higher levels of autonomy for robots and vehicles means there is an ever greater need for such systems to be aware of their surroundings. Whilst solutions already exist for creating 3D scene maps, many are based on active scanning devices such as laser scanners and depth cameras that are either expensive, unwieldy, or do not function well under certain environmental conditions. As a result passive cameras are a favoured sensor due their low cost, small size, and ability to work in a range of lighting conditions. In this work we address some of the remaining research challenges within the problem of 3D mapping around a moving platform. We utilise prior work in dense stereo imaging, Stereo Visual Odometry (SVO) and extend Structure from Motion (SfM) to create a pipeline optimised for on vehicle sensing. Using forward facing stereo cameras, we use state of the art SVO and dense stereo techniques to map the scene in front of the vehicle. With significant amounts of prior research in dense stereo, we addressed the issue of selecting an appropriate method by creating a novel evaluation technique. Visual 3D mapping of dynamic scenes from a moving platform result in duplicated scene objects. We extend the prior work on mapping by introducing a generalized dynamic object removal process. Unlike other approaches that rely on computationally expensive segmentation or detection, our method utilises existing data from the mapping stage and the findings from our dense stereo evaluation. We introduce a new SfM approach that exploits our platform motion to create a novel dense mapping process that exceeds the 3D data generation rate of state of the art alternatives. Finally, we combine dense stereo, SVO, and our SfM approach to automatically align point clouds from non-overlapping views to create a rotational and scale consistent global 3D model

NOVEL DENSE STEREO ALGORITHMS FOR HIGH-QUALITY DEPTH ESTIMATION FROM IMAGES

This dissertation addresses the problem of inferring scene depth information from a collection of calibrated images taken from different viewpoints via stereo matching. Although it has been heavily investigated for decades, depth from stereo remains a long-standing challenge and popular research topic for several reasons. First of all, in order to be of practical use for many real-time applications such as autonomous driving, accurate depth estimation in real-time is of great importance and one of the core challenges in stereo. Second, for applications such as 3D reconstruction and view synthesis, high-quality depth estimation is crucial to achieve photo realistic results. However, due to the matching ambiguities, accurate dense depth estimates are difficult to achieve. Last but not least, most stereo algorithms rely on identification of corresponding points among images and only work effectively when scenes are Lambertian. For non-Lambertian surfaces, the brightness constancy assumption is no longer valid. This dissertation contributes three novel stereo algorithms that are motivated by the specific requirements and limitations imposed by different applications. In addressing high speed depth estimation from images, we present a stereo algorithm that achieves high quality results while maintaining real-time performance. We introduce an adaptive aggregation step in a dynamic-programming framework. Matching costs are aggregated in the vertical direction using a computationally expensive weighting scheme based on color and distance proximity. We utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this process over two orders of magnitude. In addressing high accuracy depth estimation, we present a stereo model that makes use of constraints from points with known depths - the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel regularization prior is naturally integrated into a global inference framework in a principled way using the Bayes rule. Our probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate information from various sensors. In addressing non-Lambertian reflectance, we introduce a new invariant for stereo correspondence which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions - BRDFs). This invariant can be used to formulate a rank constraint on stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies

Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Rain can significantly impair the driver’s sight and affect his performance when driving in wet conditions. Evaluation of driver visibility in harsh weather, such as rain, has garnered considerable research since the advent of autonomous vehicles and the emergence of intelligent transportation systems. In recent years, advances in computer vision and machine learning led to a significant number of new approaches to address this challenge. However, the literature is fragmented and should be reorganised and analysed to progress in this field. There is still no comprehensive survey article that summarises driver visibility methodologies, including classic and recent data-driven/model-driven approaches on the windshield in rainy conditions, and compares their generalisation performance fairly. Most ADAS and AD systems are based on object detection. Thus, rain visibility plays a key role in the efficiency of ADAS/AD functions used in semi- or fully autonomous driving. This study fills this gap by reviewing current state-of-the-art solutions in rain visibility estimation used to reconstruct the driver’s view for object detection-based autonomous driving. These solutions are classified as rain visibility estimation systems that work on (1) the perception components of the ADAS/AD function, (2) the control and other hardware components of the ADAS/AD function, and (3) the visualisation and other software components of the ADAS/AD function. Limitations and unsolved challenges are also highlighted for further research

Event-Based Algorithms For Geometric Computer Vision

Event cameras are novel bio-inspired sensors which mimic the function of the human retina. Rather than directly capturing intensities to form synchronous images as in traditional cameras, event cameras asynchronously detect changes in log image intensity. When such a change is detected at a given pixel, the change is immediately sent to the host computer, where each event consists of the x,y pixel position of the change, a timestamp, accurate to tens of microseconds, and a polarity, indicating whether the pixel got brighter or darker. These cameras provide a number of useful benefits over traditional cameras, including the ability to track extremely fast motions, high dynamic range, and low power consumption. However, with a new sensing modality comes the need to develop novel algorithms. As these cameras do not capture photometric intensities, novel loss functions must be developed to replace the photoconsistency assumption which serves as the backbone of many classical computer vision algorithms. In addition, the relative novelty of these sensors means that there does not exist the wealth of data available for traditional images with which we can train learning based methods such as deep neural networks. In this work, we address both of these issues with two foundational principles. First, we show that the motion blur induced when the events are projected into the 2D image plane can be used as a suitable substitute for the classical photometric loss function. Second, we develop self-supervised learning methods which allow us to train convolutional neural networks to estimate motion without any labeled training data. We apply these principles to solve classical perception problems such as feature tracking, visual inertial odometry, optical flow and stereo depth estimation, as well as recognition tasks such as object detection and human pose estimation. We show that these solutions are able to utilize the benefits of event cameras, allowing us to operate in fast moving scenes with challenging lighting which would be incredibly difficult for traditional cameras

Algorithms for Reconstruction of hidden 3D shapes using diffused reflections

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 85-89).This thesis aims at discovering algorithms to recover the geometry of hidden objects from tertiary diffuse scattering, given time of flight information. We focus on using ultra high speed capture of photons to accurately determine information about distance light travelled and using it to infer hidden geometry. We aim at investigating issues such as the feasibility, uniqueness(in solution domain) and invertibility of this problem. We also aim at formulating the forward and inverse theory of secondary and tertiary diffuse scattering using ideas from tomography. We aim at developing tomography based approaches and sparsity based methods to recover 3D shapes of objects "around the corner". We analyze multi-bounce propagation of light in an unknown hidden volume and demonstrate that the reflected light contains sufficient information to recover the 3D structure of the hidden scene. We formulate the forward and inverse theory of secondary and tertiary scattering reflection using ideas from energy front propagation and tomography. We show that using careful choice of approximations, such as Fresnel approximation, greatly simplifies this problem and the inversion can be achieved via a backpropagation process. We provide a theoretical analysis of the invertibility, uniqueness and choices of space-time-angle dimensions using synthetic examples. We show that a 2D streak camera can be used to discover and reconstruct hidden geometry. Using a 1D high speed time of flight camera, we show that our method can be used recover 3D shapes of objects "around the corner".by Otkrist Gupta.S.M