Estimating motion, size and material properties of moving non-line-of-sight objects in cluttered environments

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 111-117).The thesis presents a framework for Non-Line-of-Sight Computer Vision techniques using wave fronts. Using short-pulse illumination and a high speed time-of-flight camera, we propose algorithms that use multi path light transport analysis to explore the environments beyond line of sight. What is moving around the corner interests everyone including a driver taking a turn, a surgeon performing laparoscopy and a soldier entering enemy base. State of the art techniques that do range imaging are limited by (i) inability to handle multiple diffused bounces [LIDAR] (ii) Wavelength dependent resolution limits [RADAR] and (iii) inability to map real life objects [Diffused Optical Tomography]. This work presents a framework for (a) Imaging the changing Space-time-impulse-responses of moving objects to pulsed illumination (b) Tracking motion along with absolute positions of these hidden objects and (c) recognizing their default properties like material and size and reflectance. We capture gated space-time impulse responses of the scene and their time differentials allow us to gauge absolute positions of moving objects with knowledge of only relative times of arrival (as absolute times are hard to synchronize at femto second intervals). Since we record responses at very short time intervals we collect multiple readings from different points of illumination and thus capturing multi-perspective responses allowing us to estimate reflectance properties. Using this, we categorize and give parametric models of the materials around corner. We hope this work inspires further exploration of NLOS computer vision techniques.by Rohit Pandharkar.S.M

Non-line-of-sight tracking of people at long range

A remote-sensing system that can determine the position of hidden objects has applications in many critical real-life scenarios, such as search and rescue missions and safe autonomous driving. Previous work has shown the ability to range and image objects hidden from the direct line of sight, employing advanced optical imaging technologies aimed at small objects at short range. In this work we demonstrate a long-range tracking system based on single laser illumination and single-pixel single-photon detection. This enables us to track one or more people hidden from view at a stand-off distance of over 50~m. These results pave the way towards next generation LiDAR systems that will reconstruct not only the direct-view scene but also the main elements hidden behind walls or corners

Computational periscopy with an ordinary digital camera

Computing the amounts of light arriving from different directions enables a diffusely reflecting surface to play the part of a mirror in a periscope—that is, perform non-line-of-sight imaging around an obstruction. Because computational periscopy has so far depended on light-travel distances being proportional to the times of flight, it has mostly been performed with expensive, specialized ultrafast optical systems^1,2,3,4,5,6,7,8,9,10,11,12. Here we introduce a two-dimensional computational periscopy technique that requires only a single photograph captured with an ordinary digital camera. Our technique recovers the position of an opaque object and the scene behind (but not completely obscured by) the object, when both the object and scene are outside the line of sight of the camera, without requiring controlled or time-varying illumination. Such recovery is based on the visible penumbra of the opaque object having a linear dependence on the hidden scene that can be modelled through ray optics. Non-line-of-sight imaging using inexpensive, ubiquitous equipment may have considerable value in monitoring hazardous environments, navigation and detecting hidden adversaries.We thank F. Durand, W. T. Freeman, Y. Ma, J. Rapp, J. H. Shapiro, A. Torralba, F. N. C. Wong and G. W. Wornell for discussions. This work was supported by the Defense Advanced Research Projects Agency (DARPA) REVEAL Program contract number HR0011-16-C-0030. (HR0011-16-C-0030 - Defense Advanced Research Projects Agency (DARPA) REVEAL Program)Accepted manuscrip

A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discotinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and VIP can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (NSF SBE-0354378); Office of Naval Research (N00014-01-1-0624

Footprints and Free Space from a Single Color Image

Understanding the shape of a scene from a single color image is a formidable computer vision task. However, most methods aim to predict the geometry of surfaces that are visible to the camera, which is of limited use when planning paths for robots or augmented reality agents. Such agents can only move when grounded on a traversable surface, which we define as the set of classes which humans can also walk over, such as grass, footpaths and pavement. Models which predict beyond the line of sight often parameterize the scene with voxels or meshes, which can be expensive to use in machine learning frameworks. We introduce a model to predict the geometry of both visible and occluded traversable surfaces, given a single RGB image as input. We learn from stereo video sequences, using camera poses, per-frame depth and semantic segmentation to form training data, which is used to supervise an image-to-image network. We train models from the KITTI driving dataset, the indoor Matterport dataset, and from our own casually captured stereo footage. We find that a surprisingly low bar for spatial coverage of training scenes is required. We validate our algorithm against a range of strong baselines, and include an assessment of our predictions for a path-planning task.Comment: Accepted to CVPR 2020 as an oral presentatio