Path Tracing Research Articles

Path Tracing in 2D, 3D, and Physicalized Networks.

It is common to advise against using 3D to visualize abstract data such as networks, however Ware and Mitchell's 2008 study showed that path tracing in a network is less error prone in 3D than in 2D. It is unclear, however, if 3D retains its advantage when the 2D presentation of a network is improved using edge-routing, and when simple interaction techniques for exploring the network are available. We address this with two studies of path tracing under new conditions. The first study was preregistered, involved 34 users, and compared 2D and 3D layouts that the user could rotate and move in virtual reality with a handheld controller. Error rates were lower in 3D than in 2D, despite the use of edge-routing in 2D and the use of mouse-driven interactive highlighting of edges. The second study involved 12 users and investigated data physicalization, comparing 3D layouts in virtual reality versus physical 3D printouts of networks augmented with a Microsoft HoloLens headset. No difference was found in error rate, but users performed a variety of actions with their fingers in the physical condition which can inform new interaction techniques.

Simulation of Optical Tactile Sensors Supporting Slip and Rotation Using Path Tracing and IMPM

Simulation of Optical Tactile Sensors Supporting Slip and Rotation Using Path Tracing and IMPM

Coarse-to-Fine Separation of Wood and Leaf from MLS Street Tree Point Clouds Using Branch Tilt Prior and Enhanced Shortest Path Tracing

Coarse-to-Fine Separation of Wood and Leaf from MLS Street Tree Point Clouds Using Branch Tilt Prior and Enhanced Shortest Path Tracing

Real-time Realistic Volume Rendering of Consistently High Quality with Dynamic Illumination.

Direct Volume Rendering (DVR) plays an important role in scientific data visualization. To generate photo-realistic DVR results, the physical light transport throughout the volume is simulated by applying the Monte Carlo-based volumetric path tracing (VPT) approach. For real-time applications, due to the time constraint for rendering each frame, only a limited number of samples shall be taken for the computation per pixel. This can result in a significant amount of noise in the rendering results. This paper describes our optimized VPT sampling algorithm and a novel denoising technique to generate consistently high-quality realistic DVR results in real time. We develop a new shading model that can reduce estimation variance to enhance the quality of DVR results. Additionally, a hybrid acceleration structure is created by integrating both octree and macrocell to improve sampling efficiency. This allows the acquisition of sufficiently more shading samples while maintaining the desired interactive frame rate. To further eliminate remaining noise and improve temporal stability of DVR results, we develop a novel spatiotemporal denoising framework. Our denoiser decouples the estimated radiance into high-detail low-noise and low-detail high-noise components. Different denoising algorithms are separately applied to these components to reduce noise without introducing blurring artifacts. Our DVR system can consistently offer high rendering quality and good temporal stability across DVR result frames in real time. During fast user interactions and with rapid alterations of the illumination condition, our rendering method can still provide good visual comfort and representation accuracy without visible latency.

State of the Art in Efficient Translucent Material Rendering with BSSRDF

AbstractSub‐surface scattering is always an important feature in translucent material rendering. When light travels through optically thick media, its transport within the medium can be approximated using diffusion theory, and is appropriately described by the bidirectional scattering‐surface reflectance distribution function (BSSRDF). BSSRDF methods rely on assumptions about object geometry and light distribution in the medium, which limits their applicability to general participating media problems. However, despite the high computational cost of path tracing, BSSRDF methods are often favoured due to their suitability for real‐time applications. We review these methods and discuss the most recent breakthroughs in this field. We begin by summarizing various BSSRDF models and then implement most of them in a 2D searchlight problem to demonstrate their differences. We focus on acceleration methods using BSSRDF, which we categorize into two primary groups: pre‐computation and texture methods. Then we go through some related topics, including applications and advanced areas where BSSRDF is used, as well as problems that are sometimes important yet are ignored in sub‐surface scattering estimation. In the end of this survey, we point out remaining constraints and challenges, which may motivate future work to facilitate sub‐surface scattering.

Comparison of fast daylighting climate-based simulation methods for parametric design: two-phase, three-phase method and path tracing

ABSTRACT Computer modeling and simulation are essential for predicting architectural solutions performance. When integrated into the design process; they are known as performance-based design. During the design process, fast analytical cycles are required. However, daylighting simulations are challenging due to computational processing and time requirements. This paper presents a comparative study of fast daylight simulation methods, two-phase, three-phase and path tracing, in a hypothetical commercial building located in Miami, Florida. Five types of static glass and one electrochromic glass were tested with six parametric models of a translucent horizontal shading device. Daysim was used as reference. The two-phase method produced results similar to the reference results with faster processing times for static facades. For dynamic facades, the path tracing method with the tested interface enabled faster simulations and parametric variations. However, the three-phase method yielded a lower average error.

Warped-Area Reparameterization of Differential Path Integrals

Physics-based differentiable rendering is becoming increasingly crucial for tasks in inverse rendering and machine learning pipelines. To address discontinuities caused by geometric boundaries and occlusion, two classes of methods have been proposed: 1) the edge-sampling methods that directly sample light paths at the scene discontinuity boundaries, which require nontrivial data structures and precomputation to select the edges, and 2) the reparameterization methods that avoid discontinuity sampling but are currently limited to hemispherical integrals and unidirectional path tracing. We introduce a new mathematical formulation that enjoys the benefits of both classes of methods. Unlike previous reparameterization work that focused on hemispherical integral, we derive the reparameterization in the path space. As a result, to estimate derivatives using our formulation, we can apply advanced Monte Carlo rendering methods, such as bidirectional path tracing, while avoiding explicit sampling of discontinuity boundaries. We show differentiable rendering and inverse rendering results to demonstrate the effectiveness of our method.

SLANG.D: Fast, Modular and Differentiable Shader Programming

We introduce SLANG.D, an extension to the Slang shading language that incorporates first-class automatic differentiation support. The new shading language allows us to transform a Direct3D-based path tracer to be fully differentiable with minor modifications to existing code. SLANG.D enables a shared ecosystem between machine learning frameworks and pre-existing graphics hardware API-based rendering systems, promoting the interchange of components and ideas across these two domains. Our contributions include a differentiable type system designed to ensure type safety and semantic clarity in codebases that blend differentiable and non-differentiable code, language primitives that automatically generate both forward and reverse gradient propagation methods, and a compiler architecture that generates efficient derivative propagation shader code for graphics pipelines. Our compiler supports differentiating code that involves arbitrary control-flow, dynamic dispatch, generics and higher-order differentiation, while providing developers flexible control of checkpointing and gradient aggregation strategies for best performance. Our system allows us to differentiate an existing real-time path tracer, Falcor, with minimal change to its shader code. We show that the compiler-generated derivative kernels perform as efficiently as handwritten ones. In several benchmarks, the SLANG.D code achieves significant speedup when compared to prior automatic differentiation systems.

Diffusion Posterior Illumination for Ambiguity-Aware Inverse Rendering

Inverse rendering, the process of inferring scene properties from images, is a challenging inverse problem. The task is ill-posed, as many different scene configurations can give rise to the same image. Most existing solutions incorporate priors into the inverse-rendering pipeline to encourage plausible solutions, but they do not consider the inherent ambiguities and the multi-modal distribution of possible decompositions. In this work, we propose a novel scheme that integrates a denoising diffusion probabilistic model pre-trained on natural illumination maps into an optimization framework involving a differentiable path tracer. The proposed method allows sampling from combinations of illumination and spatially-varying surface materials that are, both, natural and explain the image observations. We further conduct an extensive comparative study of different priors on illumination used in previous work on inverse rendering. Our method excels in recovering materials and producing highly realistic and diverse environment map samples that faithfully explain the illumination of the input images.

Efficient sound modeling for acoustic monitoring in complex temperature and wind fields

Active monitoring of atmospheric processes, combustion characteristics in burning furnaces, and various industrial applications relies on precise reconstruction of temperature and medium flow fields. This paper derives a novel trajectory equation for modeling sound wave trajectories within complex temperature and wind fields. Unlike traditional ray-equations widely used in dynamic media, the newly formulated trajectory equation is based on the propagation distance rather than the sound wave's time of flight. This alteration directly incorporates trajectory curvature, facilitating the development of efficient path tracing algorithms. In scenarios where wind speed is significantly lower than the Laplace sound speed and the wavefront's normal direction aligns closely with the sound wave's propagating direction, a simplified trajectory equation is derived. We develop a shooting method to numerically solve both the general and simplified trajectory equations. The fourth-order Runge-Kutta method (RK4) is employed, known for its high accuracy with a local fifth-order truncation error. A benchmark test, utilizing an analytically known path for the sound wave, confirms the shooting method's high precision. One notable strength of this approach lies in its computational efficiency. Tracing sound trajectories originating from a single source is achieved within seconds or tens of seconds on standard personal computers across various numerical examples. Furthermore, the shooting method effectively models sound trajectories in complex temperature and wind fields, such as those encountered in the near-surface atmosphere and high-temperature furnaces. Due to their efficiency and accuracy, the newly formulated trajectory equations and the accompanying shooting method are not only well suited for acoustic modeling, as demonstrated in this study, but are also poised for seamless integration into acoustic tomography systems in future studies.

A Differential Diffusion Theory for Participating Media

AbstractWe present a novel approach to differentiable rendering for participating media, addressing the challenge of computing scene parameter derivatives. While existing methods focus on derivative computation within volumetric path tracing, they fail to significantly improve computational performance due to the expensive computation of multiply‐scattered light. To overcome this limitation, we propose a differential diffusion theory inspired by the classical diffusion equation. Our theory enables real‐time computation of arbitrary derivatives such as optical absorption, scattering coefficients, and anisotropic parameters of phase functions. By solving derivatives through the differential form of the diffusion equation, our approach achieves remarkable speed gains compared to Monte Carlo methods. This marks the first differentiable rendering framework to compute scene parameter derivatives based on diffusion approximation. Additionally, we derive the discrete form of diffusion equation derivatives, facilitating efficient numerical solutions. Our experimental results using synthetic and realistic images demonstrate the accurate and efficient estimation of arbitrary scene parameter derivatives. Our work represents a significant advancement in differentiable rendering for participating media, offering a practical and efficient solution to compute derivatives while addressing the limitations of existing approaches.

Photorealistic Rapid Computer-Generated Holography Employing an Enhanced Path Tracing Technique With Sequence Generated Trial

Photorealistic Rapid Computer-Generated Holography Employing an Enhanced Path Tracing Technique With Sequence Generated Trial

AT-GCN: A DDoS attack path tracing system based on attack traceability knowledge base and GCN

The rapid development of the Internet provides sufficient attack entrances for massive malicious terminals, which makes it extremely challenging to effectively trace the path of DDoS attacks. Traditional solutions mainly trace distributed denial of service (DDoS) attacks by adding tags to data packet headers or querying logs, which increases the cost of tagging. There are also some works that implement attack tracking based on the network-wide perspective and centralized control of SDN, but these methods are difficult to deploy on a large scale. In order to solve this problem, we propose a DDoS attack path tracing system (AT-GCN) based on attack traceability knowledge base and graph convolutional network (GCN). We first propose the construction process of the attack traceability knowledge base, and design the intra-domain attack graph and the traceability algorithm recommendation graph to solve the problems of DDoS attack path traceability and optimal traceability solution recommendation. On this basis, we propose a GCN-based intra-domain attack traceability scheme, and design a subgraph sampling algorithm Tracing-Sample adapted to intra-domain DDoS attack traceability, aiming to efficiently use the graph structure in the knowledge base to reproduce DDoS attack paths. Additionally, we recommend the traceability algorithm based on user-based collaborative filtering (UBCF), and dynamically recommend the best traceability algorithm according to the different requirements of administrators for traceability performance. Compared with other GCN algorithms, the results show that the recall of the AT-GCN system is increased by 7.3% on average, and the FPR is reduced by 5.7% on average at the expense of the memory usage rate. Under different scale topologies, the recall of the AT-GCN system can be stabilized at 95%.

Introduction to bioimaging‐based spatial multi‐omic novel methods

In this review, we introduced five different multiplex FISH methods used for image‐based spatial multi‐omics: seqFISH+, merFISH, DNA seqFISH+, DNA merFISH, and MINA. We provided a systematic collective perspective to review these FISH methods that could significantly benefit researchers on conducting their studies in the field. Our study provided an informative survey on these multiplex FISH methods. Therefore, this review would provide better understanding for researchers in the community to help them select the proper method, in order to understand the molecular mechanism in life sciences.BackgroundSpatial multi‐omics are demonstrated to be a powerful method to assist researchers on genetic studies. In this review, bioimaging‐based spatial multi‐omics techniques such as seqFISH+, merFISH, integrated DNA seqFISH+, DNA merFISH, and MINA are introduced along with each technique’s probe design, development, and imaging processes.ResultsseqFISH employed 4–5 fluorophores to barcode and conducted multiple rounds of hybridization, in order that mRNA can be identified through color‐coding. seqFISH+ added 60 pseudo‐color and distributed them equally into three channels to enhance imaging power, in order that i.e., 24,000 genes can be imaged in total. merFISH utilized 4 out 16 Hamming distance to innovatively provide a robust error‐detecting method. MINA, a methodology combining merFISH (multiplexed error‐robust fluorescence in situ hybridization) and chromosomal tracing, enabled multiplexed genomic architecture imaged in mammalian single cells. Optical reconstruction of chromatin architecture (ORCA) a method that could conduct DNA path tracing in nanoscale manner with kilobase resolution, an FISH variation that improved genetic resolution, enable high‐precision fiducial registration and sequential imaging, and utilized Oligopaint probe to hybridize the short genomic region ranging from 2 to 10 kilobase. ORCA then prescribes these short section primary probes with individual barcodes to attach fluorophore and to be imaged.ConclusionThis review concentrated on providing a comprehensive overview for these spatial‐multi‐omics techniques with the intention on helping researchers on selecting appropriate technique for their research.

NeLT: Object-Oriented Neural Light Transfer

This article presents object-oriented neural light transfer (NeLT), a novel neural representation of the dynamic light transportation between an object and the environment. Our method disentangles the global illumination of a scene into individual objects’ light transportation represented via neural networks, then composes them explicitly. It therefore enables flexible rendering with dynamic lighting, cameras, materials, and objects. Our rendering features various important global illumination effects, such as diffuse illumination, glossy illumination, dynamic shadowing, and indirect illumination, which completes the capability of existing neural object representation. Experiments show that NeLT does not require path tracing or shading results as input but achieves rendering quality comparable to state-of-the-art rendering frameworks, including the recent deep learning based denoisers.

Space-Based THz Radar Fly-Around Imaging Simulation for Space Targets Based on Improved Path Tracing

Aiming at the space target detection application of a space-based terahertz (THz) radar, according to the imaging mechanism of broadband THz radars, a THz radar imaging simulation method based on improved path tracing is proposed. Firstly, the characterization method of THz scattering characteristics based on Kirchhoff’s approximation method is introduced. The multi-parameter THz bidirectional reflectance distribution function (THz-BRDF) models of aluminum (Al), white-painted Al, and polyimide film at 0.215 THz are fitted according to the theoretical data, with fitting errors below 4%. Then, the THz radar imaging simulation method based on improved path tracing is presented in detail. The simulation method utilizes path tracing to simulate parallelized THz radar echo signal data, considering multi-path energy scattering based on the THz-BRDF model. Finally, we conducted THz radar imaging simulation experiments. The influences in the imaging process of different fly-around motions are analyzed, and a comparison experiment is conducted with the fast-physical optics (FPO) method. The comparative results indicate that the proposed method exhibits richer and more realistic features compared with the FPO method. The simulation experiments results demonstrate that the proposed method can provide a data source for ground algorithm testing of THz radars, particularly in evaluating the target detection and recognition algorithm based on deep learning, providing strong support for the application of space-based THz radars in the future.

Non-homogeneous denoising for virtual reality in real-time path tracing rendering

Real time Path-tracing is becoming an important approach for the future of games, digital entertainment, and virtual reality applications that require realism and immersive environments. Among different possible optimizations, denoising Monte Carlo rendered images is necessary in low sampling densities. When dealing with Virtual Reality devices, other possibilities can also be considered, such as foveated rendering techniques. Hence, this work proposes a novel and promising rendering pipeline for denoising a real-time path-traced application in a dual-screen system such as head-mounted display (HMD) devices. Therefore, we leverage characteristics of the foveal vision by computing G-Buffers with the features of the scene and a buffer with the foveated distribution for both left and right screens. Later, we path trace the image within the coordinates buffer generating only a few initial rays per selected pixel, and reconstruct the noisy image output with a novel non-homogeneous denoiser that accounts for the pixel distribution. Our experiments showed that this proposed rendering pipeline could achieve a speedup factor up to 1.35 compared to one without our optimizations.

Data Parallel Multi‐GPU Path Tracing using Ray Queue Cycling

AbstractWe propose a novel approach to data‐parallel path tracing on single‐node/multi‐GPU hardware that builds on ray forwarding, but which aims—above all else—at generality and practicability. We do this by avoiding any attempts at reducing the number of traces or forward operations performed, and instead focus on always using all GPUs' aggregate compute and bandwidth to effectively trace each ray on every GPU. We show that—counter‐intuitively—this is both feasible and desirable; and that when run on typical data‐center/cloud hardware, the resulting framework not only achieves good performance and scalability, but also comes with significantly fewer limitations, assumptions, or preprocessing requirements than existing techniques.

MILO: Multi-Bounce Inverse Rendering for Indoor Scene With Light-Emitting Objects.

Recently, many advances in inverse rendering are achieved by high-dimensional lighting representations and differentiable rendering. However, multi-bounce lighting effects can hardly be handled correctly in scene editing using high-dimensional lighting representations, and light source model deviation and ambiguities exist in differentiable rendering methods. These problems limit the applications of inverse rendering. In this paper, we present a multi-bounce inverse rendering method based on Monte Carlo path tracing, to enable correct complex multi-bounce lighting effects rendering in scene editing. We propose a novel light source model that is more suitable for light source editing in indoor scenes, and design a specific neural network with corresponding disambiguation constraints to alleviate ambiguities during the inverse rendering. We evaluate our method on both synthetic and real indoor scenes through virtual object insertion, material editing, relighting tasks, and so on. The results demonstrate that our method achieves better photo-realistic quality.

GazeGraphVis: Visual analytics of gaze behaviors at multiple graph levels for path tracing tasks

Graph visualization contributes to an efficient understanding of interconnected properties in graph data. However, the exponential growth of interconnections poses great challenges to the efficient visual cognition of graph data. Generation of expressive graph visualization requires investigations of the cognitive process of exploring graph visualization, which can be revealed through the analysis of gaze behaviors. In this paper, we propose GazeGraphVis, a visual analytics system, to analyze gaze behaviors for path tracing tasks. Specifically, GazeGraphVis visualizes gaze behaviors at multiple levels of graph, node and edge with overview + detail techniques to provide a comprehensive analysis of human cognitive processes when finishing path tracing tasks. The insights of gaze behaviors for path tracing tasks are revealed using an integrated multiple-view interface. Domain experts in visualization and eye tracking analysis gave high praise to GazeGraphVis for its capability of obtaining the overall search tendencies and deeply analyzing the factors that affect gaze behaviors.