- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Selective guided sampling with complete light transport paths
摘要： Finding good global importance sampling strategies for Monte Carlo light transport is challenging. While estimators using local methods (such as BSDF sampling or next event estimation) often work well in the majority of a scene, small regions in path space can be sampled insufficiently (e.g. a reflected caustic). We propose a novel data-driven guided sampling method which selectively adapts to such problematic regions and complements the unguided estimator. It is based on complete transport paths, i.e. is able to resolve the correlation due to BSDFs and free flight distances in participating media. It is conceptually simple and places anisotropic truncated Gaussian distributions around guide paths to reconstruct a continuous probability density function (guided PDF). Guide paths are iteratively sampled from the guided as well as the unguided PDF and only recorded if they cause high variance in the current estimator. While plain Monte Carlo samples paths independently and Markov chain-based methods perturb a single current sample, we determine the reconstruction kernels by a set of neighbouring paths. This enables local exploration of the integrand without detailed balance constraints or the need for analytic derivatives. We show that our method can decompose the path space into a region that is well sampled by the unguided estimator and one that is handled by the new guided sampler. In realistic scenarios, we show 4× speedups over the unguided sampler.
关键词： Sampling and Reconstruction,Global Illumination,Stochastic Sampling,Monte Carlo
更新于2025-09-23 15:23:52
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Learning to reconstruct shape and spatially-varying reflectance from a single image
摘要： Reconstructing shape and reflectance properties from images is a highly under-constrained problem, and has previously been addressed by using specialized hardware to capture calibrated data or by assuming known (or highly constrained) shape or reflectance. In contrast, we demonstrate that we can recover non-Lambertian, spatially-varying BRDFs and complex geometry belonging to any arbitrary shape class, from a single RGB image captured under a combination of unknown environment illumination and flash lighting. We achieve this by training a deep neural network to regress shape and reflectance from the image. Our network is able to address this problem because of three novel contributions: first, we build a large-scale dataset of procedurally generated shapes and real-world complex SVBRDFs that approximate real world appearance well. Second, single image inverse rendering requires reasoning at multiple scales, and we propose a cascade network structure that allows this in a tractable manner. Finally, we incorporate an in-network rendering layer that aids the reconstruction task by handling global illumination effects that are important for real-world scenes. Together, these contributions allow us to tackle the entire inverse rendering problem in a holistic manner and produce state-of-the-art results on both synthetic and real data.
关键词： rendering layer,global illumination,deep learning,SVBRDF,single image,flash light,cascade network
更新于2025-09-23 15:23:52
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A radiative transfer framework for non-exponential media
摘要： We develop a new theory of volumetric light transport for media with non-exponential free-flight distributions. Recent insights from atmospheric sciences and neutron transport demonstrate that such distributions arise in the presence of correlated scatterers, which are naturally produced by processes such as cloud condensation and fractal-pattern formation. Our theory formulates a non-exponential path integral as the result of averaging stochastic classical media, and we introduce practical models to solve the resulting averaging problem efficiently. Our theory results in a generalized path integral which allows us to handle non-exponential media using the full range of Monte Carlo rendering algorithms while enriching the range of achievable appearance. We propose parametric models for controlling the statistical correlations by leveraging work on stochastic processes, and we develop a method to combine such unresolved correlations (and the resulting non-exponential free-flight behavior) with explicitly modeled macroscopic heterogeneity. This provides a powerful authoring approach where artists can freely design the shape of the attenuation profile separately from the macroscopic heterogeneous density, while our theory provides a physically consistent interpretation in terms of a path space integral. We address important considerations for graphics including reciprocity and bidirectional rendering algorithms, all in the presence of surfaces and correlated media.
关键词： non-exponential transport,global illumination,participating media,volume rendering,radiative transfer
更新于2025-09-23 15:23:52
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Geometry-aware metropolis light transport
摘要： Markov chain Monte Carlo (MCMC) rendering utilizes a sequence of correlated path samples which is obtained by iteratively mutating the current state to the next. The efficiency of MCMC rendering depends on how well the mutation strategy is designed to adapt to the local structure of the state space. We present a novel MCMC rendering method that automatically adapts the step sizes of the mutations to the geometry of the rendered scene. Our geometry-aware path space perturbation largely avoids tentative samples with zero contribution due to occlusion. Our method limits the mutation step size by estimating the maximum opening angle of a cone, centered around a segment of a light transport path, where no geometry obstructs visibility. This geometry-aware mutation increases the acceptance rates, while not degrading the sampling quality. As this cone estimation introduces a considerable overhead if done naively, to make our approach efficient, we discuss and analyze fast approximate methods for cone angle estimation which utilize the acceleration structure already present for the ray-geometry intersection. Our new approach, integrated into the framework of Metropolis light transport, can achieve results with lower error and less artifact in equal time compared to current path space mutation techniques.
关键词： global illumination,Markov chain Monte Carlo light transport
更新于2025-09-23 15:23:52
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[ACM Press the 2018 Conference - Honolulu, Hawaii (2018.10.09-2018.10.12)] Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems - RACS '18 - Efficient synthetic light field generation using adaptive multi-level rendering
摘要： Real-time global illumination rendering is very desirable for emerging applications such as Virtual Reality (VR) and Augmented Reality (AR). However, client devices have difficulties to support photo-realistic rendering, such as Ray-Tracing, due to insufficient computing resources. Many modern frameworks adopted Light Field rendering to support device displaying. A Light Field can be pre-computed and store in cloud. During runtime, the display extracts the colors from the Light Field to generate arbitrary real time viewpoints or re-focusing within a predefined area. To efficiently compute the Light Field, We have combined DIBR (Depth-Image-Based-Rendering) and traditional ray-tracing in an adaptive fashion to synthesize images. By measuring the color errors during runtime, we adaptively determine the right balance between DIBR and Ray Tracing. To further optimize the computation efficiency, we also added a multi-level design to exploit the degree of shareable pixels among images to control the computation for error removal. Experiments show that we achieved up to 3.24X speedup in Light Field generation for relative simple scenes like Cornell Box, and about 2X speed up for complex scenes like Conference Room or Sponza.
关键词： Light Field,Ray-Tracing,Image-Based Rendering,Global Illumination
更新于2025-09-11 14:15:04
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Automated phenotyping of epicuticular waxes of grapevine berries using light separation and convolutional neural networks
摘要： The epicuticular wax represents the outer layer of the grape berry skin and is known as trait that is significantly correlated to resilience towards Botrytis bunch rot. Traditionally this trait is classified using the OIV descriptor 227 (berry bloom) in a time consuming way resulting in subjective and error-prone phenotypic data. In the present study an objective, fast and sensor-based approach was developed to monitor epicuticular waxes. From the technical point-of-view, it is known that the measurement of different illumination components conveys important information about observed object surfaces. A Light-Separation-Lab is proposed in order to capture illumination-separated images of grapevine berries for phenotyping the distribution of epicuticular waxes (berry bloom). For image analysis, an efficient convolutional neural network approach is used to derive the uniformity and intactness of waxes on berries. Method validation over six grapevine cultivars shows accuracies up to 97.3%. In addition, electrical impedance of the cuticle and its epicuticular waxes (described as an indicator for the thickness of berry skin and its permeability) was correlated to the detected proportion of waxes with r = 0.76. This novel, fast and non-invasive phenotyping approach facilitates enlarged screenings within grapevine breeding material and genetic repositories regarding berry bloom characteristics and its impact on resilience towards Botrytis bunch rot.
关键词： Botrytis cinerea,Berry bloom,Convolutional Neural Networks (CNN),Vitis vinifera,Direct and global illumination
更新于2025-09-10 09:29:36
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[ACM Press SIGGRAPH Asia 2018 Posters - Tokyo, Japan (2018.12.04-2018.12.07)] SIGGRAPH Asia 2018 Posters on - SA '18 - Spectral rendering of fluorescence using importance sampling
摘要： Spectral rendering is necessary for rendering a scene with fluorescence, because fluorescence is a strongly wavelength dependent phenomenon. We propose a method for rendering fluorescence under global illumination environment efficiently by using importance sampling of wavelength considering both spectra of fluorescent materials and light sources.
关键词： global illumination,spectral rendering,importance sampling,Fluorescence
更新于2025-09-09 09:28:46