Technological Progression and Procedures in Microsoft Kinect Sensor

Kinect is a sensor technology capable of tracking any environmental phenomena by sensing it for identification. And it plays a unique and vital role in the study of identification as it recognizes every object using the sensor. However, Kinect sensors track and sense objects using their color and data with the enhancement of tracking various actions and postures. Improvisations are inhabited within the technology based on identity, digitalization, alpha channels, depth of color and sensors in Autism research. Major concepts of Kinect sensor and its enhancement features are surveyed in this paper

Introduction to the Issue on Emerging Techniques in 3-D

Cataloged from PDF version of article.The fifteen papers in this special section that focus on three dimensional content (3D), with particular emphasis on the fusion of conventional camera outputs with those captured by other modalities, such as active sensors, multi-spectral data or dynamic range images as well as applications that support the measurement and improvement of 3-D content

Temporal View Synthesis of Dynamic Scenes through 3D Object Motion Estimation with Multi-Plane Images

The challenge of graphically rendering high frame-rate videos on low compute devices can be addressed through periodic prediction of future frames to enhance the user experience in virtual reality applications. This is studied through the problem of temporal view synthesis (TVS), where the goal is to predict the next frames of a video given the previous frames and the head poses of the previous and the next frames. In this work, we consider the TVS of dynamic scenes in which both the user and objects are moving. We design a framework that decouples the motion into user and object motion to effectively use the available user motion while predicting the next frames. We predict the motion of objects by isolating and estimating the 3D object motion in the past frames and then extrapolating it. We employ multi-plane images (MPI) as a 3D representation of the scenes and model the object motion as the 3D displacement between the corresponding points in the MPI representation. In order to handle the sparsity in MPIs while estimating the motion, we incorporate partial convolutions and masked correlation layers to estimate corresponding points. The predicted object motion is then integrated with the given user or camera motion to generate the next frame. Using a disocclusion infilling module, we synthesize the regions uncovered due to the camera and object motion. We develop a new synthetic dataset for TVS of dynamic scenes consisting of 800 videos at full HD resolution. We show through experiments on our dataset and the MPI Sintel dataset that our model outperforms all the competing methods in the literature.Comment: To appear in ISMAR 2022; Project website: https://nagabhushansn95.github.io/publications/2022/DeCOMPnet.htm