Dual-Shutter Optical Vibration Sensing.

Visual vibrometry is a highly useful tool for remote capture of audio, as well as the physical properties of materials, human heart rate, and more. While visually-observable vibrations can be captured directly with a high-speed camera, minute imperceptible object vibrations can be optically amplified by imaging the displacement of a speckle pattern created by shining a laser beam on the vibrating surface. In this paper, we propose a novel method for sensing vibrations at high speeds (up to 63 kHz), for multiple scene sources at once, using sensors rated for only 130 Hz operation. Our method relies on simultaneously capturing the scene with two cameras equipped with rolling and global shutter sensors, respectively. The rolling shutter camera captures distorted speckle images that encode the high-speed object vibrations. The global shutter camera captures undistorted reference images of the speckle pattern, helping to decode the source vibrations. We demonstrate our method by capturing vibration caused by audio sources (e.g., speakers, human voice, and musical instruments) and analyzing the vibration modes of a tuning fork.

Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in the Wild.

Bundle adjustment jointly optimizes camera intrinsics and extrinsics and 3D point triangulation to reconstruct a static scene. The triangulation constraint, however, is invalid for moving points captured in multiple unsynchronized videos and bundle adjustment is not designed to estimate the temporal alignment between cameras. We present a spatiotemporal bundle adjustment framework that jointly optimizes four coupled sub-problems: estimating camera intrinsics and extrinsics, triangulating static 3D points, as well as sub-frame temporal alignment between cameras and computing 3D trajectories of dynamic points. Key to our joint optimization is the careful integration of physics-based motion priors within the reconstruction pipeline, validated on a large motion capture corpus of human subjects. We devise an incremental reconstruction and alignment algorithm to strictly enforce the motion prior during the spatiotemporal bundle adjustment. This algorithm is further made more efficient by a divide and conquer scheme while still maintaining high accuracy. We apply this algorithm to reconstruct 3D motion trajectories of human bodies in dynamic events captured by multiple uncalibrated and unsynchronized video cameras in the wild. To make the reconstruction visually more interpretable, we fit a statistical 3D human body model to the asynchronous video streams. Compared to the baseline, the fitting significantly benefits from the proposed spatiotemporal bundle adjustment procedure. Because the videos are aligned with sub-frame precision, we reconstruct 3D motion at much higher temporal resolution than the input videos. Website: http://www.cs.cmu.edu/~ILIM/projects/IM/STBA.

Self-Supervised Multi-View Person Association and its Applications.

Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams. To solve this problem, reliable association of the same person across distant viewpoints and temporal instances is essential. We present a self-supervised framework to adapt a generic person appearance descriptor to the unlabeled videos by exploiting motion tracking, mutual exclusion constraints, and multi-view geometry. The adapted discriminative descriptor is used in a tracking-by-clustering formulation. We validate the effectiveness of our descriptor learning on WILDTRACK T. Chavdarova et al., "WILDTRACK: A multi-camera HD dataset for dense unscripted pedestrian detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2018, pp. 5030-5039. and three new complex social scenes captured by multiple cameras with up to 60 people "in the wild". We report significant improvement in association accuracy (up to 18 percent) and stable and coherent 3D human skeleton tracking (5 to 10 times) over the baseline. Using the reconstructed 3D skeletons, we cut the input videos into a multi-angle video where the image of a specified person is shown from the best visible front-facing camera. Our algorithm detects inter-human occlusion to determine the camera switching moment while still maintaining the flow of the action well. Website: http://www.cs.cmu.edu/~ILIM/projects/IM/Association4Tracking.

Programmable Non-Epipolar Indirect Light Transport: Capture and Analysis.

The decomposition of light transport into direct and global components, diffuse and specular interreflections, and subsurface scattering allows for new visualizations of light in everyday scenes. In particular, indirect light contains a myriad of information about the complex appearance of materials useful for computer vision and inverse rendering applications. In this paper, we present a new imaging technique that captures and analyzes components of indirect light via light transport using a synchronized projector-camera system. The rectified system illuminates the scene with epipolar planes corresponding to projector rows, and we vary two key parameters to capture plane-to-ray light transport between projector row and camera pixel: (1) the offset between projector row and camera row in the rolling shutter (implemented as synchronization delay), and (2) the exposure of the camera row. We describe how this synchronized rolling shutter performs illumination multiplexing, and develop a nonlinear optimization algorithm to demultiplex the resulting 3D light transport operator. Using our system, we are able to capture live short and long-range non-epipolar indirect light transport, disambiguate subsurface scattering, diffuse and specular interreflections, and distinguish materials according to their subsurface scattering properties. In particular, we show the utility of indirect imaging for capturing and analyzing the hidden structure of veins in human skin.

Overcoming the tradeoff between confinement and focal distance using virtual ultrasonic optical waveguides.

A conventional optical lens can be used to focus light into the target medium from outside, without disturbing the medium. The focused spot size is proportional to the focal distance in a conventional lens, resulting in a tradeoff between penetration depth in the target medium and spatial resolution. We have shown that virtual ultrasonically sculpted gradient-index (GRIN) optical waveguides can be formed in the target medium to steer light without disturbing the medium. Here, we demonstrate that such virtual waveguides can relay an externally focused Gaussian beam of light through the medium beyond the focal distance of a single external physical lens, to extend the penetration depth without compromising the spot size. Moreover, the spot size can be tuned by reconfiguring the virtual waveguide. We show that these virtual GRIN waveguides can be formed in transparent and turbid media, to enhance the confinement and contrast ratio of the focused beam of light at the target location. This method can be extended to realize complex optical systems of external physical lenses and in situ virtual waveguides, to extend the reach and flexibility of optical methods.

Texture Illumination Separation for Single-Shot Structured Light Reconstruction.

Active illumination based methods have a trade-off between acquisition time and resolution of the estimated 3D shapes. Multi-shot approaches can generate dense reconstructions but require stationary scenes. Single-shot methods are applicable to dynamic objects but can only estimate sparse reconstructions and are sensitive to surface texture. We present a single-shot approach to produce dense shape reconstructions of highly textured objects illuminated by one or more projectors. The key to our approach is an image decomposition scheme that can recover the illumination image of different projectors and the texture images of the scene from their mixed appearances. We focus on three cases of mixed appearances: the illumination from one projector onto textured surface, illumination from multiple projectors onto a textureless surface, or their combined effect. Our method can accurately compute per-pixel warps from the illumination patterns and the texture template to the observed image. The texture template is obtained by interleaving the projection sequence with an all-white pattern. The estimated warps are reliable even with infrequent interleaved projection and strong object deformation. Thus, we obtain detailed shape reconstruction and dense motion tracking of the textured surfaces. The proposed method, implemented using a one camera and two projectors system, is validated on synthetic and real data containing subtle non-rigid surface deformations.

Beyond perspective dual photography with illumination masks.

Scene appearance from the point of view of a light source is called a reciprocal or dual view. Since there exists a large diversity in illumination, these virtual views may be nonperspective and multiviewpoint in nature. In this paper, we demonstrate the use of occluding masks to recover these dual views, which we term shadow cameras. We first show how to render a single reciprocal scene view by swapping the camera and light source positions. We then extend this technique for multiple views by both building a virtual shadow camera array and by exploiting area sources. We also capture nonperspective views such as orthographic, cross-slit and a pushbroom variant, while introducing novel applications such as converting between camera projections and removing refractive and catadioptric distortions. Finally, since a shadow camera is artificial, we can manipulate any of its intrinsic parameters, such as camera skew, to create perspective distortions. We demonstrate a variety of indoor and outdoor results and show a rendering application for capturing the light-field of a light-source.

Translucent Radiosity: Efficiently Combining Diffuse Inter-Reflection and Subsurface Scattering.

It is hard to efficiently model the light transport in scenes with translucent objects for interactive applications. The inter-reflection between objects and their environments and the subsurface scattering through the materials intertwine to produce visual effects like color bleeding, light glows, and soft shading. Monte-Carlo based approaches have demonstrated impressive results but are computationally expensive, and faster approaches model either only inter-reflection or only subsurface scattering. In this paper, we present a simple analytic model that combines diffuse inter-reflection and isotropic subsurface scattering. Our approach extends the classical work in radiosity by including a subsurface scattering matrix that operates in conjunction with the traditional form factor matrix. This subsurface scattering matrix can be constructed using analytic, measurement-based or simulation-based models and can capture both homogeneous and heterogeneous translucencies. Using a fast iterative solution to radiosity, we demonstrate scene relighting and dynamically varying object translucencies at near interactive rates.

Appearance derivatives for isonormal clustering of scenes.

A new technique is proposed for scene analysis, called "appearance clustering." The key result of this approach is that the scene points can be clustered according to their surface normals, even when the geometry, material, and lighting are all unknown. This is achieved by analyzing an image sequence of a scene as it is illuminated by a smoothly moving distant light source. In such a scenario, the brightness measurements at each pixel form a "continuous appearance profile." When the source path follows an unstructured trajectory (obtained, say, by smoothly hand-waving a light source), the locations of the extrema of the appearance profile provide a strong cue for the scene point's surface normal. Based on this observation, a simple transformation of the appearance profiles and a distance metric are introduced that, together, can be used with any unsupervised clustering algorithm to obtain isonormal clusters of a scene. We support our algorithm empirically with comprehensive simulations of the Torrance-Sparrow and Oren-Nayar analytic BRDFs, as well as experiments with 25 materials obtained from the MERL database of measured BRDFs. The method is also demonstrated on 45 examples from the CURET database, obtaining clusters on scenes with real textures such as artificial grass and ceramic tile, as well as anisotropic materials such as satin and velvet. The results of applying our algorithm to indoor and outdoor scenes containing a variety of complex geometry and materials are shown. As an example application, isonormal clusters are used for lighting-consistent texture transfer. Our algorithm is simple and does not require any complex lighting setup for data collection.

Enhancing resolution along multiple imaging dimensions using assorted pixels.

Multisampled imaging is a general framework for using pixels on an image detector to simultaneously sample multiple dimensions of imaging (space, time, spectrum, brightness, polarization, etc.). The mosaic of red, green, and blue spectral filters found in most solid-state color cameras is one example of multisampled imaging. We briefly describe how multisampling can be used to explore other dimensions of imaging. Once such an image is captured, smooth reconstructions along the individual dimensions can be obtained using standard interpolation algorithms. Typically, this results in a substantial reduction of resolution (and, hence, image quality). One can extract significantly greater resolution in each dimension by noting that the light fields associated with real scenes have enormous redundancies within them, causing different dimensions to be highly correlated. Hence, multisampled images can be better interpolated using local structural models that are learned offline from a diverse set of training images. The specific type of structural models we use are based on polynomial functions of measured image intensities. They are very effective as well as computationally efficient. We demonstrate the benefits of structural interpolation using three specific applications. These are 1) traditional color imaging with a mosaic of color filters, 2) high dynamic range monochrome imaging using a mosaic of exposure filters, and 3) high dynamic range color imaging using a mosaic of overlapping color and exposure filters.

Polarization-based vision through haze.

We present an approach for easily removing the effects of haze from passively acquired images. Our approach is based on the fact that usually the natural illuminating light scattered by atmospheric particles (airlight) is partially polarized. Optical filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, stems from physics-based analysis that works under a wide range of atmospheric and viewing conditions, even if the polarization is low. The approach does not rely on specific scattering models such as Rayleigh scattering and does not rely on the knowledge of illumination directions. It can be used with as few as two images taken through a polarizer at different orientations. As a byproduct, the method yields a range map of the scene, which enables scene rendering as if imaged from different viewpoints. It also yields information about the atmospheric particles. We present experimental results of complete dehazing of outdoor scenes, in far-from-ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color.