Image Sequence Geolocation with Human Travel Priors [PAPER] [PAGE]
E. Kalogerakis, O. Vesselova, J. Hays, A. Efros, A. Hertzmann
Proceedings of the IEEE International Conference on Computer Vision 2009, Kyoto, Japan (oral presentation)

Abstract: This paper presents a method for estimating geographic location for sequences of time-stamped photographs. A prior distribution over travel describes the likelihood of traveling from one location to another during a given time interval. This distribution is based on a training database of 6 million photographs from Flickr.com. An image likelihood for each location is defined by matching a test photograph against the training database. Inferring location for images in a test sequence is then performed using the Forward-Backward algorithm, and the model can be adapted to individual users as well. Using temporal constraints allows our method to geolocate images without recognizable landmarks, and images with no geographic cues whatsoever. This method achieves a substantial performance improvement over the best-available baseline, and geolocates some users’ images with near-perfect accuracy.

Data-driven curvature for real-time line drawing of dynamic scenes [PAPER] [VIDEO][PAGE]
E. Kalogerakis, D. Nowrouzezahrai, P. Simari, J. McCrae, A. Hertzmann, K. Singh
ACM Transactions on Graphics, Vol. 28, No. 1, January 2009
(also presented in SIGGRAPH 2009, New Orleans, USA, August 3 - 9, 2009)

Abstract: This paper presents a method for real-time line drawing of deforming objects. Object-space line drawing algorithms for many types of curves, including suggestive contours, highlights, ridges and valleys, rely on surface curvature and curvature derivatives. Unfortunately, these curvatures and their derivatives cannot be computed in real-time for animated, deforming objects. In a preprocessing step, our method learns the mapping from a low-dimensional set of animation parameters (e.g., joint angles) to surface curvatures for a deforming 3D mesh. The learned model can then accurately and efficiently predict curvatures and their derivatives, enabling real-time object-space rendering of suggestive contours and other such curves. This represents an order-of-magnitude speed-up over the fastest existing algorithm capable of estimating curvatures and their derivatives accurately enough for many different types of line drawings. The learned model can generalize to novel animation sequences, and is also very compact, typically requiring a few megabytes of storage at run-time. We demonstrate our method for various types of animated objects, including skeleton-based characters, cloth simulation and blend-shape facial animation, using a variety of non-photorealistic rendering styles.An important component of our system is the use of dimensionality reduction for differential mesh data.

Multi-objective shape segmentation and labeling [PAPER] [VIDEO]
P. Simari, D. Nowrouzezahrai, E. Kalogerakis, K. Singh
Special Issue of the Computer Graphics Forum, Vol. 28, No. 5, August 2009
(also presented in Eurographics Symposium of Geometry Processing 2009, Berlin, Germany, July 15-17)

Abstract: Shape segmentations designed for different applications show significant variation in the composition of their parts. In this paper, we introduce the segmentation and labeling of shape based on the simultaneous optimization of multiple heterogenous objectives that capture application-specific segmentation criteria. We present a number of efficient objective functions that capture useful shape adjectives (compact, flat, narrow, perpendicular, etc.) Segmentation descriptions within our framework combine multiple such objective functions with optional labels to define each part. The optimization problem is simplified by proposing weighted Voronoi partitioning as a compact and continuous parametrization of spatially embedded shape segmentations. Separation of spatially close but geodesically distant parts is made possible using multi-dimensional scaling prior to Voronoi partitioning. Optimization begins with an initial segmentation found using the centroids of a k-means clustering of surface elements. This partition is automatically labeled to optimize heterogeneous part objectives and the Voronoi centers and their weights optimized using Generalized Pattern Search. We illustrate our framework using several diverse segmentation applications: consistent segmentations with semantic labels, bounding volume hierarchies for path tracing, and automatic rig and clothing transfer between animation characters.

Shadowing Dynamic Scenes with Arbitrary BRDFs [PAPER] [VIDEO]
D. Nowrouzezahrai, E. Kalogerakis, E. Fiume
Special Issue of the Computer Graphics Forum, Vol. 28, No. 2, April 2009
(also presented in Eurographics 2009, Berlin, Germany, March 30 - April 3)

Abstract: We present a real-time relighting and shadowing method for dynamic scenes with varying lighting, view and BRDFs. Our approach is based on a compact representation of reflectance data that allows for changing the BRDF at run-time and a data-driven method for accurately synthesizing self-shadows on articulated and deformable geometries. Unlike previous self-shadowing approaches, we do not rely on local blocking heuristics. We do not fit a model to the BRDF-weighted visibility, but rather only to the visibility that changes during animation. In this manner, our model is more compact than previous techniques and requires less computation both during fitting and at run-time. Our reflectance product operators can re-integrate arbitrary low-frequency view-dependent BRDF effects on-the-fly and are compatible with all previous dynamic visibility generation techniques as well as our own data-driven visibility model. We apply our reflectance product operators to three different visibility generation models, and our data-driven model can achieve framerates well over 300Hz.

Extracting lines of curvature from noisy point clouds [PAPER][PAGE]
E. Kalogerakis, D. Nowrouzezahrai, P. Simari, K. Singh
Special Issue of the Computer-Aided Design on Point-Based Computational Techniques, Vol. 41, No. 4, April 2009

Abstract: We present a robust framework for extracting lines of curvature from point clouds. First, we show a novel approach to denoising the input point cloud using robust statistical estimates of surface normal and curvature which automatically rejects outliers and corrects points by energy minimization. Then the lines of curvature are constructed on the point cloud with controllable density. Our approach is applicable to surfaces of arbitrary genus, with or without boundaries, and is statistically robust to noise and outliers while preserving sharp surface features. We show our approach to be eective over a range of synthetic and real-world input datasets with varying amounts of noise and outliers. The extraction of curvature information can benefit many applications in CAD, computer vision and graphics for point cloud shape analysis, recognition and segmentation. Here, we show the possibility of using the lines of curvature for feature-preserving mesh construction directly from noisy point clouds.