Volume Ray Tracing Research Articles

Spatiotemporally Consistent HDR Indoor Lighting Estimation

We propose a physically motivated deep learning framework to solve a general version of the challenging indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Particularly, when the input is an LDR video sequence, our framework not only progressively refines the lighting prediction as it sees more regions, but also preserves temporal consistency by keeping the refinement smooth. Our framework reconstructs a spherical Gaussian lighting volume (SGLV) through a tailored 3D encoder-decoder, which enables spatially consistent lighting prediction through volume ray tracing, a hybrid blending network for detailed environment maps, an in-network Monte Carlo rendering layer to enhance photorealism for virtual object insertion, and recurrent neural networks (RNN) to achieve temporally consistent lighting prediction with a video sequence as the input. For training, we significantly enhance the OpenRooms public dataset of photorealistic synthetic indoor scenes with around 360k HDR environment maps of much higher resolution and 38k video sequences, rendered with GPU-based path tracing. Experiments show that our framework achieves lighting prediction with higher quality compared to state-of-the-art single-image or video-based methods, leading to photorealistic AR applications such as object insertion.

GPU-optimized volume ray tracing for massive numbers of rays in radiotherapy

Ray tracing within a uniform grid volume is a fundamental process invoked frequently by many applications, especially radiation-dose calculation methods in radiotherapy. However, the conflicting features between the GPU memory architecture and the memory-accessing patterns of volume ray tracing lead to inefficient usage of GPU memory bandwidth and waste of capability of modern GPUs. To improve the ray tracing performance on GPU, we propose a lookup-table-based ray tracing method which is specially optimized towards the GPU memory system for processing a massive number of rays. The proposed method is based on a key observation that many of these applications normally involves a massive number of rays, but their ray tracing may not need to follow a specific execution order. Therefore, we divide the 3D space into many regions (called pyramids) and group together the rays falling into the same pyramid. For each ray group, the volume is rotated and resampled for their raytracing. This divide-and-rotate strategy allows the memory access of the ray tracing process to adopt a table-lookup approach and leads to better memory coalescing on GPU. Our proposed method was thoroughly evaluated in four volume setups with randomly-generated rays. The collapsed-cone convolution/superposition (CCCS) dose calculation method is also implemented with/without the proposed approach to verify the feasibility of our method. Compared with the direct GPU implementation of the popular 3DDDA algorithm, our method provides a speedup in the range of 1.91--2.94X for the volume settings we used. Major performance factors, including ray origins, volume size, and pyramid size, are also analyzed. The proposed technique was also found to be able to give a speedup of 1.61--2.17X over the original GPU implementation of the CCCS algorithm. Our experiment results indicate that the proposed approach is capable of offering better coalesced memory access which eventually boosts the raytracing performance on GPU. Moreover, our approach is conceptually simple and can be readily included into various applications.

Efficient CPU‐based Volume Ray Tracing Techniques

Abstract Recent research on high‐performance ray tracing has achieved real‐time performance even for highly complex surface models already on a single PC. In this report, we provide an overview of techniques for extending real‐time ray tracing also to interactive volume rendering. We review fast rendering techniques for different volume representations and rendering modes in a variety of computing environments. The physically‐based rendering approach of ray tracing enables high image quality and allows for easily mixing surface, volume and other primitives in a scene, while fully accounting for all of their optical interactions. We present optimized implementations and discuss the use of upcoming high‐performance processors for volume ray tracing.

Evaluation of a real-time direct volume rendering system

The results of evaluating VIRIM, that can be considered as the first real-time direct volume rendering system for ray-casting and volume ray-tracing, is described in this paper. Emphasis is laid on experiences concerning the hardware architecture used with respect to the anticipated application area in medicine. The issues are the flexibility of VIRIM, the restriction to two gradient components only, the duplication of the volume data sets on different modules, the size of the volume data set, the gray-value segmentation tool, and the support of algorithmic improvements like space-leaping, early ray-termination and others. It turned out that flexibility gives the main benefits since it allows easy response to different demands during integration into clinical routines. Given this flexibility the application areas of real-time rendering systems increase dramatically: most of the user requirements focus now not on visualization but on general volume data processing. The most serious bottleneck of VIRIM is the limited volume memory that is integrated in the first prototype. The gray-value segmentation tool turned out to be very valuable. It is highly useful if original, i.e. unsegmented data have to be dealt with, and if pre-segmented data have to be investigated. All other benefits and architectural shortcomings are not critical for the application areas of VIRIM, i.e. operation simulation and control in head surgery.

Data parallel volume-rendering algorithms for interactive visualization

The increasing availability of parallel computing platforms has led to the development of parallel volume-rendering algorithms. In the present paper we compare two algorithms for volume raytracing in a data-parallel framework: a shearing technique and a line-drawing technique. The two algorithms are primarily distinguished by the level of parallelism they exploit. Both algorithms have been implemented on the Connection Machine CM2 massively parallel computer and execute at speeds suitable for interactive volume-rendering applications. Since considerable floating-point resources are available on the CM2, we have used rendering algorithms based on transport theory. In the second part of the paper we examine some of the tradeoffs involved between image quality and rendering speed when using high-fidelity rendering algorithms.

Fresnel volume ray tracing

The concept of “Fresnel volume ray tracing” consists of standard ray tracing, supplemented by a computation of parameters defining the first Fresnel zones at each point of the ray. The Fresnel volume represents a 3-D spatial equivalent of the Fresnel zone that can also be called a physical ray. The shape of the Fresnel volume depends on the position of the source and the receiver, the structure between them, and the type of body wave under consideration. In addition, the shape also depends on frequency: it is narrow for a high frequency and thick for a low frequency. An efficient algorithm for Fresnel volume ray tracing, based on the paraxial ray method, is proposed. The evaluation of the parameters defining the first Fresnel zone merely consists of a simple algebraic manipulation of the elements of the ray propagator matrix. The proposed algorithm may be applied to any high‐frequency seismic body wave propagating in a laterally varying 2-D or 3-D layered structure (P, S, converted, multiply reflected, etc.). Numerical examples of Fresnel volume ray tracing in 2-D inhomogeneous layered structures are presented. Certain interesting properties of Fresnel volumes are discussed (e.g., the double caustic effect). Fresnel volume ray tracing offers numerous applications in seismology and seismic prospecting. Among others, it can be used to study the resolution of the seismic method and the validity conditions of the ray method.