In this article, we discuss enhanced full 360° 3D reconstruction of dynamic scenes containing non-rigidly deforming objects using data acquired from commodity depth or 3D cameras. Several approaches for enhanced and full 3D reconstruction of non-rigid objects have been proposed in the literature. These approaches suffer from several limitations due to requirement of a template, inability to tackle large local deformations and topology changes, inability to tackle highly noisy and low-resolution data, and inability to produce online results. We target online and template-free enhancement of the quality of noisy and low-resolution full 3D reconstructions of dynamic non-rigid objects. For this purpose, we propose a view-independent recursive and dynamic multi-frame 3D super-resolution scheme for noise removal and resolution enhancement of 3D measurements. The proposed scheme tracks the position and motion of each 3D point at every timestep by making use of the current acquisition and the result of the previous iteration. The effects of system blur due to per-point tracking are subsequently tackled by introducing a novel and efficient multi-level 3D bilateral total variation regularization. These characteristics enable the proposed scheme to handle large deformations and topology changes accurately. A thorough evaluation of the proposed scheme on both real and simulated data is carried out. The results show that the proposed scheme improves upon the performance of the state-of-the-art methods and is able to accurately enhance the quality of low-resolution and highly noisy 3D reconstructions while being robust to large local deformations.

One of the most crucial requirements for building a multi-view system is the estimation of relative poses of all cameras. An approach tailored for a RGB-D cameras based multi-view system is missing. We propose BAICP+ which combines Bundle Adjustment (BA) and Iterative Closest Point (ICP) algorithms to take into account both 2D visual and 3D shape information in one minimization formulation to estimate relative pose parameters of each camera. BAICP+ is generic enough to take different types of visual features into account and can be easily adapted to varying quality of 2D and 3D data. We perform experiments on real and simulated data. Results show that with the right weighting factor BAICP+ has an optimal performance when compared to BA and ICP used independently or sequentially.

Heat and air conditioning losses in buildings and factories lead to a large amount of wasted energy. The Action Plan for Energy Efficiency [4] of the European Commission estimates that the largest cost-effective energy savings potential lies in residential (≈ 27%) and commercial (≈ 30%) buildings. Imagine a technology that creates a precise digital 3D model of heat distribution and heat flow enabling one to detect all sources of wasted energy and to modify buildings to reach these savings. This video presents our approach to this task. Methods for creating a consistent laser scan model enhanced with information from thermal and optical cameras are presented.

In this paper, we describe MPEGTool, an X window-based tool that can be used to generate a motion picture expert group (MPEG) encoded bit stream for video sequences and to study the statistical properties of the encoded data. The tool was designed to study the characteristics of variable bit rate video sources for transmission over asynchronous transfer mode (ATM) based broadband integrated services digital network (B-ISDN). The tool, which has a window-based graphic user interface, allows a user to specify several of the MPEG parameters such as the intraframe-to-interframe ratio and the quantizer scale. The tool also includes a statistical package that allows the user to plot graphs of various statistics including bit distributions, ATM cell distributions, time se ries, autocorrelation functions and cell interarrival times.