Latest News & Updates


Our paper on single-view shape from shadow motion was accepted to ICCP 2014.


Our paper on single-view shape from shadow correspondence was accepted to CVPR 2013.


Our paper on simultaneous photometric stereo and radiometric response estimation for outdoor webcams was accepted to ECCV 2012.


Our journal article for calibrating outdoor imagery will soon appear in ACM TOMCCAP.


I am a Ph. D. student at Washington University in St. Louis, working under the supervision of Robert Pless in the Media and Machines Lab. Before starting my graduate work at Washington University, I received my BS in Computer Science from Truman State University in 2009.

My research focuses on interpreting very long term time-lapse imagery taken from tens of thousands of publicly-available outdoor webcam images, by geographically calibrating them with a user in the loop, and recovering the 3D structure of the scene from a single view.

Selected Publications

Structure from Shadow Motion

Austin Abrams, Ian Schillebeeckx, Robert Pless.

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In outdoor images, cast shadows define 3D constraints between the sun, the points casting a shadow, and the surfaces onto which shadows are cast. This cast shadow structure provides a powerful cue for 3D reconstruction, but requires that shadows be tracked over time, and this is difficult as shadows have minimal texture. Thus, we develop a shadow tracking system that enforces geometric consistency for each track and then combines thousands of tracking results to create a 3D model of scene geometry. We demonstrate reconstruction results on a variety of outdoor scenes, including some that show the 3D structure of occluders never directly observed by the camera.
To be presented at the International Conference on Computational Photography 2014.
Download the .pdf (6 MB) Supplemental Video

The Episolar Constraint: Monocular Shape from Shadow Correspondence

Austin Abrams, Kylia Miskell, Robert Pless.

Shadows encode a powerful geometric cue: if one pixel casts a shadow onto another, then the two pixels are colinear with the lighting direction. Given many images over many lighting directions, this constraint can be leveraged to recover the depth of a scene from a single viewpoint. For outdoor scenes with solar illumination, we term this the episolar constraint, which provides a convex optimization to solve for the sparse depth of a scene from shadow correspondences, a method to reduce the search space when finding shadow correspondences, and a method to geometrically calibrate a camera using shadow constraints. Our method constructs a dense network of nonlocal constraints which complements recent work on outdoor photometric stereo and cloud based cues for 3D. We demonstrate results across a variety of time-lapse sequences from webcams "in the wild."
To be presented at IEEE Computer Vision and Pattern Recognition (CVPR) 2013.
Download the .pdf (9 MB)

Heliometric Stereo: Shape from Sun Position

Austin Abrams, Christopher Hawley, Robert Pless.

In this work, we present a method to uncover shape from webcams "in the wild." We present a variant of photometric stereo which uses the sun as a distant light source, so that lighting direction can be computed from known GPS and timestamps. We propose an iterative, non-linear optimization process that optimizes the error in reproducing all images from an extended time-lapse with an image formation model that accounts for ambient lighting, shadows, changing light color, dense surface normal maps, radiometric calibration, and exposure. Unlike many approaches to uncalibrated outdoor image analysis, this procedure is automatic, and we report quantitative results by comparing extracted surface normals to Google Earth 3D models. We evaluate this procedure on data from a varied set of scenes and emphasize the advantages of including imagery from many months.
Presented at European Conference on Computer Vision (ECCV) 2012.
Download the .pdf (3 MB) (Author's version. The original publication is available at

Web-Accessible Geographic Integration and Calibration of Webcams

Austin Abrams, Robert Pless.

A global network of webcams offers unique viewpoints from tens of thousands of locations. Understanding the geographic context of this imagery is vital in using these cameras for quantitative environmental monitoring or surveillance applications. We derive robust geo-calibration constraints that allow users to geo-register static or pan-tilt-zoom cameras by specifying a few corresponding points, and describe our web interface suitable for novices. We discuss design decisions that support our scalable, publicly-accessible web service that allows webcam textures to be displayed live on 3D geographic models. Finally, we demonstrate several multimedia applications for geocalibrated cameras.
Published in ACM TOMCCAP, February 2013.
Download the .pdf of pre-publication version(40 MB)

Exploratory Analysis of Time-Lapse Imagery with Fast Subset PCA

Austin Abrams, Emily Feder, Robert Pless

In surveillance and environmental monitoring applications, it is common to have millions of images of a particular scene. While there exist tools to find particular events, anomalies, human actions and behaviors, there has been little investigation of tools which allow more exploratory searches in the data. This paper proposes modifications to PCA that enable users to quickly recompute low-rank decompositions for select spatial and temporal subsets of the data. This process returns decompositions orders of magnitude faster than general PCA and are close to optimal in terms of reconstruction error. We show examples of real exploratory data analysis across several applications, including an interactive web application.
Presented at IEEE Workshop on Applications of Computer Vision (WACV) 2011.
Download the .pdf (3 MB)

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