We present a statistical framework to merge the information from silhouettes segmented in multiple view images to infer the 3D shape of an object. The approach is generalising the robust but discrete modelling of the visual hull by using the concept of averaged likelihoods. One resulting advantage of our framework is that the objective function is continuous and therefore an iterative gradient ascent algorithm can be defined to efficiently search the space. Moreover this results in a method which is less memory demanding and one that is very suitable to a parallel processing architecture. Experimental results shows that this approach is efficient for getting a robust initial guess to the 3D shape of an object in view.

Ray-tracing algorithms are known for producing highly realistic images, but at a significant computational cost. For this reason, a large body of research exists on various techniques for accelerating these costly algorithms. One approach to achieving superior performance which has received comparatively little attention is the design of specialised ray-tracing hardware. The research that does exist on this topic has consistently demonstrated that significant performance and efficiency gains can be achieved with dedicated microarchitectures. However, previous work on hardware ray-tracing has focused almost entirely on the traversal and intersection aspects of the pipeline. As a result, the critical aspect of the management and construction of acceleration data-structures remains largely absent from the hardware literature.We propose that a specialised microarchitecture for this purpose could achieve considerable performance and efficiency improvements over programmable platforms. To this end, we have developed the first dedicated microarchitecture for the construction of binned SAH BVHs. Cycle-accurate simulations show that our design achieves significant improvements in raw performance and in the bandwidth required for construction, as well as large efficiency gains in terms of performance per clock and die area compared to manycore implementations. We conclude that such a design would be useful in the context of a heterogeneous graphics processor, and may help future graphics processor designs to reduce predicted technology-imposed utilisation limits.

Figure 1: Screenshots of our framework executing a variety of benchmarks, each with the angular velocity of the simulated objects uniformdistributed between (-0.25, -0.25, -0.25) and (0.25, 0.25, 0.25) AbstractCollision detection is a vital component of applications spanning myriad fields, yet there exists no means for developers to analyse the suitability of their collision detection algorithms across the spectrum of scenarios that could be encountered. To rectify this, we propose a framework for benchmarking interactive collision detection, which consists of a single generic benchmark that can be adapted using a number of parameters to create a large range of practical benchmarks. This framework allows algorithm developers to test the validity of their algorithms across a wide test space and allows developers of interactive applications to recreate their application scenarios and quickly determine the most amenable algorithm. To demonstrate the utility of our framework, we adapted it to work with three collision detection algorithms supplied with the Bullet Physics SDK. Our results demonstrate that those algorithms conventionally believed to offer the best performance are not always the correct choice. This demonstrates that conventional wisdom cannot be relied on for selecting a collision detection algorithm and that our benchmarking framework fulfils a vital need in the collision detection community. The framework has been made open source, so that developers do not have to reprogram the framework to test their own algorithms, allowing for consistent results across different algorithms and reducing development time.

We present a novel and effective skeletonization algorithm for binary and gray-scale images, based on the anisotropic heat diffusion analogy. We diffuse the image in the direction normal to the feature boundaries and also allow tangential diffusion (curvature decreasing diffusion) to contribute slightly. The proposed anisotropic diffusion provides a high quality medial function in the image: it removes noise and preserves prominent curvatures of the shape along the level-sets (skeleton features). The skeleton strength map, which provides the likelihood of a point to be part of the skeleton, is defined by the mean curvature measure. Finally, thin and binary skeleton is obtained by non-maxima suppression and hysteresis thresholding of the skeleton strength map. Our method outperforms the most related and the popular methods in skeleton extraction especially in noisy conditions. Results show that the proposed approach is better at handling noise in images and preserving the skeleton features at the centerline of the shape.

We present a Direct Volume Rendering method that makes use of newly available Nvidia graphics hardware for Bounding Volume Hierarchies. Using BVHs for DVR has been overlooked in recent research due to build times potentially impeding interactive rates. We indicate that this is not necessarily the case, especially when a clustering algorithm is applied before the BVH build to reduce leaf‐node complexity. Our results show substantial render time improvements for full‐resolution DVR on GPU in comparison to a recent state‐of‐the‐art approach for empty‐space‐skipping. Furthermore, the use of a BVH for DVR allows seamless integration into popular surface‐based path‐tracing technologies like Nvidia's OptiX.