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Dellaert, Frank

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Joint Semantic Segmentation and 3D Reconstruction from Monocular Video

2014-09 , Kundu, Abhijit , Li, Yin , Dellaert, Frank , Li, Fuxin , Rehg, James M.

We present an approach for joint inference of 3D scene structure and semantic labeling for monocular video. Starting with monocular image stream, our framework produces a 3D volumetric semantic + occupancy map, which is much more useful than a series of 2D semantic label images or a sparse point cloud produced by traditional semantic segmentation and Structure from Motion(SfM) pipelines respectively. We derive a Conditional Random Field (CRF) model defined in the 3D space, that jointly infers the semantic category and occupancy for each voxel. Such a joint inference in the 3D CRF paves the way for more informed priors and constraints, which is otherwise not possible if solved separately in their traditional frameworks. We make use of class specific semantic cues that constrain the 3D structure in areas, where multiview constraints are weak. Our model comprises of higher order factors, which helps when the depth is unobservable. We also make use of class specific semantic cues to reduce either the degree of such higher order factors, or to approximately model them with unaries if possible. We demonstrate improved 3D structure and temporally consistent semantic segmentation for diffcult, large scale, forward moving monocular image sequence.

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An MCMC-based Particle Filter for Tracking Multiple Interacting Targets

2004-05 , Khan, Zia , Balch, Tucker , Dellaert, Frank

We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In response, we developed a joint tracker that includes a more sophisticated motion model to maintain the identity of targets throughout an interaction, drastically reducing tracker failures. The paper presents two main contributions: (1) we show how a Markov random field (MRF) motion prior, built on the fly at each time step, can substantially improve tracking when targets interact, and (2) we show how this can be done efficiently using Markov chain Monte Carlo (MCMC) sampling. We prove that incorporating an MRF to model interactions is equivalent to adding an additional interaction factor to the importance weights in a joint particle filter. Since a joint particle filter suffers from exponential complexity in the number of tracked targets, we replace the traditional importance sampling step in the particle filter with an MCMC sampling step. The resulting filter deals efficiently and effectively with complicated interactions when targets approach each other. We present both qualitative and quantitative results to substantiate the claims made in the paper, including a large scale experiment on a video-sequence of over 10,000 frames in length.

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The Bayes Tree: An Algorithmic Foundation for Probabilistic Robot Mapping

2010-12 , Kaess, Michael , Ila, Viorela , Roberts, Richard , Dellaert, Frank

We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three insights provided by our new data structure. First, the Bayes tree provides a better understanding of batch matrix factorization in terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, that combines incremental updates with fluid relinearization of a reduced set of variables for efficiency, combined with fast convergence to the exact solution. We also present a novel strategy for incremental variable reordering to retain sparsity.We evaluate our algorithm on standard datasets in both landmark and pose SLAM settings.

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MCMC-based Multiview Reconstruction of Piecewise Smooth Subdivision Curves with a Variable Number of Control Points

2004-05 , Kaess, Michael , Zboinski, Rafal , Dellaert, Frank

We investigate the automated reconstruction of piecewise smooth 3D curves, using subdivision curves as a simple but flexible curve representation. This representation allows tagging corners to model nonsmooth features along otherwise smooth curves. We present a reversible jump Markov chain Monte Carlo approach which obtains an approximate posterior distribution over the number of control points and tags. In a Rao-Blackwellization scheme, we integrate out the control point locations, reducing the variance of the resulting sampler. We apply this general methodology to the reconstruction of piecewise smooth curves from multiple calibrated views, in which the object is segmented from the background using a Markov random field approach. Results are shown for multiple images of two pot shards as would be encountered in archaeological applications.

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A Rao-Blackwellized Parts-Constellation Tracker

2005 , Schindler, Grant , Dellaert, Frank

We present a method for efficiently tracking objects represented as constellations of parts by integrating out the shape of the model. Parts-based models have been successfully applied to object recognition and tracking. However, the high dimensionality of such models present an obstacle to traditional particle filtering approaches. We can efficiently use parts-based models in a particle filter by applying Rao-Blackwellization to integrate out continuous parameters such as shape. This allows us to maintain multiple hypotheses for the pose of an object without the need to sample in the high-dimensional spaces in which partsbased models live. We present experimental results for a challenging biological tracking task.

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