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We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training,(More)
RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. RGB-D cameras rely on either structured light patterns combined with stereo sensing [6,10] or time-of-flight laser sensing [1] to generate depth estimates that can be associated with RGB pixels. Very soon, small, high-quality RGB-D cameras developed for(More)
RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D(More)
The 2006 Language Engineering Workshop Open Source Toolkit for Statistical Machine Translation had the objective to advance the current state-of-the-art in statistical machine translation through richer input and richer annotation of the training data. The workshop focused on three topics: factored translation models, confusion network decoding, and the(More)
As robots become more ubiquitous and capable of performing complex tasks, the importance of enabling untrained users to interact with them has increased. In response, unconstrained natural-language interaction with robots has emerged as a significant research area. We discuss the problem of parsing natural language commands to actions and control structures(More)
3-D motion estimation is a fundamental problem that has far-reaching implications in robotics. A scene flow formulation is attractive as it makes no assumptions about scene complexity, object rigidity, or camera motion. RGB-D cameras provide new information useful for computing dense 3-D flow in challenging scenes. In this work we show how to generalize(More)
The performance of indoor robots that stay in a single environment can be enhanced by gathering detailed knowledge of objects that frequently occur in that environment. We use an inexpensive sensor providing dense color and depth, and fuse information from multiple sensing modalities to detect changes between two 3-D maps. We adapt a recent SLAM technique(More)
We introduce an algorithm for object discovery from RGB-D (color plus depth) data, building on recent progress in using RGB-D cameras for 3-D reconstruction. A set of 3-D maps are built from multiple visits to the same scene. We introduce a multi-scene MRF model to detect objects that moved between visits, combining shape, visibility, and color cues. We(More)
The incorporation of randomness is critical for the believability and effectiveness of controllers for characters in competitive games. We present a fully automatic method for generating intelligent real-time controllers for characters in such a game. Our approach uses game theory to deal with the ramifications of the characters acting simultaneously, and(More)
We build on recent fast and accurate 3-D reconstruction techniques to segment objects during scene reconstruction. We take object outline information from change detection to build 3-D models of rigid objects and represent the scene as static and dynamic components. Object models are updated online during mapping, and can integrate segmentation information(More)