QuadStack: An Efficient Representation and Direct Rendering of Layered Datasets.

We introduce QuadStack, a novel algorithm for volumetric data compression and direct rendering. Our algorithm exploits the data redundancy often found in layered datasets which are common in science and engineering fields such as geology, biology, mechanical engineering, medicine, etc. QuadStack first compresses the volumetric data into vertical stacks which are then compressed into a quadtree that identifies and represents the layered structures at the internal nodes. The associated data (color, material, density, etc.) and shape of these layer structures are decoupled and encoded independently, leading to high compression rates (4× to 54× of the original voxel model memory footprint in our experiments). We also introduce an algorithm for value retrieving from the QuadStack representation and we show that the access has logarithmic complexity. Because of the fast access, QuadStack is suitable for efficient data representation and direct rendering. We show that our GPU implementation performs comparably in speed with the state-of-the-art algorithms (18-79 MRays/s in our implementation), while maintaining a significantly smaller memory footprint.

Real-Time External Labeling of Ghosted Views.

We present a new algorithm for calculating the external labeling of ghosted views of moderately complex 3D models. The algorithm uses multiple criteria decision making, based on fuzzy logic, to optimize positions of the labels associated with different parts of the input model. The proposed method can be used with various existing algorithms for creating ghosted views from 3D models. The method operates in real-time, which allows the user to acquire a good understanding of the structure of the input model by studying the model and its labels from different viewpoints. We have conducted a user study to evaluate label layouts produced by our algorithm and those created by humans. The results show that the proposed method can significantly improve user understanding of labeled ghosted views of complicated 3D models, and its label layouts are comparable with label layouts created by humans.

Parallel Locally-Ordered Clustering for Bounding Volume Hierarchy Construction.

We propose a novel massively parallel construction algorithm for Bounding Volume Hierarchies (BVHs) based on locally-ordered agglomerative clustering. Our method builds the BVH iteratively from bottom to top by merging a batch of cluster pairs in each iteration. To efficiently find the neighboring clusters, we keep the clusters ordered along the Morton curve. This ordering allows us to identify approximate nearest neighbors very efficiently and in parallel. We implemented our algorithm in CUDA and evaluated it in the context of GPU ray tracing. For complex scenes, our method achieves up to a twofold reduction of build times while providing up to 17 percent faster trace times compared with the state-of-the-art methods.

Lightdrum-Portable Light Stage for Accurate BTF Measurement on Site.

We propose a miniaturised light stage for measuring the bidirectional reflectance distribution function (BRDF) and the bidirectional texture function (BTF) of surfaces on site in real world application scenarios. The main principle of our lightweight BTF acquisition gantry is a compact hemispherical skeleton with cameras along the meridian and with light emitting diode (LED) modules shining light onto a sample surface. The proposed device is portable and achieves a high speed of measurement while maintaining high degree of accuracy. While the positions of the LEDs are fixed on the hemisphere, the cameras allow us to cover the range of the zenith angle from 0 ∘ to 75 ∘ and by rotating the cameras along the axis of the hemisphere we can cover all possible camera directions. This allows us to take measurements with almost the same quality as existing stationary BTF gantries. Two degrees of freedom can be set arbitrarily for measurements and the other two degrees of freedom are fixed, which provides a tradeoff between accuracy of measurements and practical applicability. Assuming that a measured sample is locally flat and spatially accessible, we can set the correct perpendicular direction against the measured sample by means of an auto-collimator prior to measuring. Further, we have designed and used a marker sticker method to allow for the easy rectification and alignment of acquired images during data processing. We show the results of our approach by images rendered for 36 measured material samples.

Optomechanical design of a portable compact bidirectional texture function measurement instrument.

Recent developments in optoelectronics and material processing techniques make it possible to design and produce a portable and compact measurement instrument for bidirectional texture function (BTF). Parallelized optics, on-board data processing, rapid prototyping, and other nonconventional production techniques and materials were the key to building an instrument capable of in situ measurements with fast data acquisition. We designed, built, and tested a prototype of a unique portable and compact multi-camera system for BTF measurement which is capable of in situ measurement of temporally unstable samples. In this paper, we present its optomechanical design.

Parallel On-Demand Hierarchy Construction on Contemporary GPUs.

We present the first parallel on-demand spatial hierarchy construction algorithm targeting ray tracing on many-core processors such as GPUs. The method performs simultaneous ray traversal and spatial hierarchy construction focused on the parts of the data structure being traversed. The method is based on a versatile framework built around a task pool and runs entirely on the GPU. We show that the on-demand construction can improve rendering times compared to full hierarchy construction. We evaluate our method on both object (BVH) and space (kd-tree) subdivision data structures and compare them mutually. The on-demand method is particularly beneficial for rendering large scenes with high occlusion. We also present SAH kd-tree builder that outperforms previous state-of-the-art builders running on the GPU.

Visibility-driven mesh analysis and visualization through graph cuts.

In this paper we present an algorithm that operates on a triangular mesh and classifies each face of a triangle as either inside or outside. We present three example applications of this core algorithm: normal orientation, inside removal, and layer-based visualization. The distinguishing feature of our algorithm is its robustness even if a difficult input model that includes holes, coplanar triangles, intersecting triangles, and lost connectivity is given. Our algorithm works with the original triangles of the input model and uses sampling to construct a visibility graph that is then segmented using graph cut.