Efficient Hardware Acceleration of Robust Volumetric Light Transport Simulation

Efficiently simulating the full range of light effects in arbitrary input scenes that contain participating media is a difficult task. Unified points, beams and paths (UPBP) is an algorithm capable of capturing a wide range of media effects, by combining bidirectional path tracing (BPT) and photon density estimation (PDE) with multiple importance sampling (MIS). A computationally expensive task of UPBP is the MIS weight computation, performed each time a light path is formed. We derive an efficient algorithm to compute the MIS weights for UPBP, which improves over previous work by eliminating the need to iterate over the path vertices. We achieve this by maintaining recursive quantities as subpaths are generated, from which the subpath weights can be computed. In this way, the full path weight can be computed by only using the data cached at the two vertices at the ends of the subpaths. Furthermore, a costly part of PDE is the search for nearby photon points and beams. Previous work has shown that a spatial data structure for photon mapping can be implemented using the hardware-accelerated bounding volume hierarchy of NVIDIA's RTX GPUs. We show that the same technique can be applied to different types of volumetric PDE and compare the performance of these data structures with the state of the art. Finally, using our new algorithm and data structures we fully implement UPBP on the GPU which we, to the best of our knowledge, are the first to do so

An Unified Multiscale Framework for Planar, Surface, and Curve Skeletonization

Computing skeletons of 2D shapes, and medial surface and curve skeletons of 3D shapes, is a challenging task. In particular, there is no unified framework that detects all types of skeletons using a single model, and also produces a multiscale representation which allows to progressively simplify, or regularize, all skeleton types. In this paper, we present such a framework. We model skeleton detection and regularization by a conservative mass transport process from a shape's boundary to its surface skeleton, next to its curve skeleton, and finally to the shape center. The resulting density field can be thresholded to obtain a multiscale representation of progressively simplified surface, or curve, skeletons. We detail a numerical implementation of our framework which is demonstrably stable and has high computational efficiency. We demonstrate our framework on several complex 2D and 3D shapes

Feature preserving noise removal for binary voxel volumes using 3D surface skeletons

Skeletons are well-known descriptors that capture the geometry and topology of 2D and 3D shapes. We leverage these properties by using surface skeletons to remove noise from 3D shapes. For this, we extend an existing method that removes noise, but keeps important (salient) corners for 2D shapes. Our method detects and removes large-scale, complex, and dense multiscale noise patterns that contaminate virtually the entire surface of a given 3D shape, while recovering its main (salient) edges and corners. Our method can treat any (voxelized) 3D shapes and surface-noise types, is computationally scalable, and has one easy-to-set parameter. We demonstrate the added-value of our approach by comparing our results with several known 3D shape denoising methods