Bochang Moon

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Bounding volume hierarchies (BVHs) are widely used to accelerate the performance of various geometric and graphics applications. These applications include ray tracing, collision detection, visibility queries, dynamic simulation, and motion planning. These applications typically precompute BVHs of input models and traverse the BVHs at runtime in order to(More)
We present a cache-oblivious ray reordering method for ray tracing. Many global illumination methods such as path tracing and photon mapping use ray tracing and generate lots of rays to simulate various realistic visual effects. However, these rays tend to be very incoherent and show lower cache utilizations during ray tracing of models. In order to address(More)
We propose an efficient and robust image-space denoising method for noisy images generated by Monte Carlo ray tracing methods. Our method is based on two new concepts: virtual flash images and homogeneous pixels. Inspired by recent developments in flash photography, virtual flash images emulate photographs taken with a flash, to capture various features of(More)
Monte Carlo ray tracing is considered one of the most effective techniques for rendering photo-realistic imagery, but requires a large number of ray samples to produce converged or even visually pleasing images. We develop a novel image-plane adaptive sampling and reconstruction method based on local regression theory. A novel local space estimation process(More)
Ray tracing and collision detection are widely used for providing high-quality visualizations and user interactions. In these algorithms, we need to detect intersecting primitives between two input objects (e.g., a ray and a 3D object in ray tracing and two 3D objects in collision detection). In order to efficiently detect these intersecting primitives,(More)
Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by(More)
We propose a new adaptive rendering algorithm that enhances the performance of Monte Carlo ray tracing by reducing the noise, i.e., variance, while preserving a variety of high-frequency edges in rendered images through a novel prediction based reconstruction. To achieve our goal, we iteratively build multiple, but sparse linear models. Each linear model(More)
In this paper, we propose a new adaptive rendering method to improve the performance of Monte Carlo ray tracing, by reducing noise contained in rendered images while preserving high-frequency edges. Our method locally approximates an image with polynomial functions and the optimal order of each polynomial function is estimated so that our reconstruction(More)
Perceptually lossless foveated rendering methods exploit human perception by selectively rendering at different quality levels based on eye gaze (at a lower computational cost) while still maintaining the user's perception of a full quality render. We consider three foveated rendering methods and propose practical rules of thumb for each method to achieve(More)