Dustin Freeman

Bio: Dustin Freeman is an academic researcher from University of Toronto. The author has contributed to research in topics: Improvisation & Gesture. The author has an hindex of 8, co-authored 18 publications receiving 2419 citations. Previous affiliations of Dustin Freeman include Microsoft.

KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera

Abstract: KinectFusion enables a user holding and moving a standard Kinect camera to rapidly create detailed 3D reconstructions of an indoor scene. Only the depth data from Kinect is used to track the 3D pose of the sensor and reconstruct, geometrically precise, 3D models of the physical scene in real-time. The capabilities of KinectFusion, as well as the novel GPU-based pipeline are described in full. Uses of the core system for low-cost handheld scanning, and geometry-aware augmented reality and physics-based interactions are shown. Novel extensions to the core GPU pipeline demonstrate object segmentation and user interaction directly in front of the sensor, without degrading camera tracking or reconstruction. These extensions are used to enable real-time multi-touch interactions anywhere, allowing any planar or non-planar reconstructed physical surface to be appropriated for touch.

ShadowGuides: visualizations for in-situ learning of multi-touch and whole-hand gestures

Abstract: We present ShadowGuides, a system for in-situ learning of multi-touch and whole-hand gestures on interactive surfaces. ShadowGuides provides on-demand assistance to the user by combining visualizations of the user's current hand posture as interpreted by the system (feedback) and available postures and completion paths necessary to finish the gesture (feedforward). Our experiment compared participants learning gestures with ShadowGuides to those learning with video-based instruction. We found that participants learning with ShadowGuides remembered more gestures and expressed significantly higher preference for the help system.

Teaching gestures with offset contact silhouettes

Abstract: A method for providing multi-touch input training on a display surface is disclosed. A touch input is detected at one or more regions of the display surface. A visualization of the touch input is displayed at a location of the display surface offset from the touch input. One or more annotations are displayed at a location of the display surface offset from the touch input and proximate to the visualization, where each annotation shows a different legal continuation of the touch input.

Integrating Multimedia Tools to Enrich Interactions in Live Streaming for Language Learning

Abstract: Online language lessons have adopted live broadcasted videos to provide more real-time interactive experiences between language teachers and learners. However, learner interactions are primarily limited to the built-in text chat in the live stream. Using text alone, learners cannot get feedback on important aspects of a language, such as speaking skills, that are afforded only by offering richer types of interactions. We present results from a 2-week in-the-wild study, in which we investigate the use of text, audio, video, image, and stickers as interaction tools for language teachers and learners in live streaming. Our language teacher explored three different teaching strategies over four live streamed English lessons, while nine students watched and interacted using multimodal tools. The findings reveal that multimodal communication yields instant feedback and increased engagement, but its use is dependent on factors such as group size, surroundings, time, and online identity.

Freehand pose-based Gestural Interaction: Studies and implications for interface design

Abstract: Most work on Freehand Gestural Interaction has focused on high-energy expressive interfaces for expert user. In this work, we examine the use of hand poses in laid-back freehand gestural interactions for novice users and examine the factors that impact gesture performance. Through two Wizard-of-Oz studies, one leading to the other, we observe how novice users behave under relaxed conditions. The first study explores the ease of use of a pose-based hand gesture vocabulary in the context of a photo-browsing task, and examines some of the key factors that impact the performance of such pose based gestures. The second explores pose-based interaction techniques for widget manipulation tasks. These studies reveal that while hand poses have the potential to expand the vocabulary of gestures and are easy to recall and use, there are a number of issues that show up in actual performance related to inadvertent modifications in hand pose and hand trajectories. We summarize the implications of these findings for the design of pose-based freehand gestural interfaces, which we believe would be useful for both interaction designers and gesture recognition researchers.

KinectFusion: Real-time dense surface mapping and tracking

Abstract: We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real-time. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse-to-fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame-to-frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real-time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR), in particular, it allows dense surfaces to be reconstructed in real-time, with a level of detail and robustness beyond any solution yet presented using passive computer vision.

打磨Using Language,倡导新理念

Abstract: 人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

Flow The Psychology Of Optimal Experience

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Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications

Abstract: Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.

SUN RGB-D: A RGB-D scene understanding benchmark suite

Abstract: Although RGB-D sensors have enabled major break-throughs for several vision tasks, such as 3D reconstruction, we have not attained the same level of success in high-level scene understanding. Perhaps one of the main reasons is the lack of a large-scale benchmark with 3D annotations and 3D evaluation metrics. In this paper, we introduce an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks. Our dataset is captured by four different sensors and contains 10,335 RGB-D images, at a similar scale as PASCAL VOC. The whole dataset is densely annotated and includes 146,617 2D polygons and 64,595 3D bounding boxes with accurate object orientations, as well as a 3D room layout and scene category for each image. This dataset enables us to train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias.