An efficient ray tracing method is presented for calculating interreflections between surfaces with both diffuse and specular components. A Monte Carlo technique computes the indirect contributions to illuminance at locations chosen by the rendering process. The indirect illuminance values are averaged over surfaces and used in place of a constant "ambient" term. Illuminance calculations are made only for those areas participating in the selected view, and the results are stored so that subsequent views can reuse common values. The density of the calculation is adjusted to maintain a constant accuracy, permitting less populated portions of the scene to be computed quickly. Successive reflections use proportionally fewer samples, which speeds the process and provides a natural limit to recursion. The technique can also model diffuse transmission and illumination from large area sources, such as the sky.

A ray tracing solution for diffuse interreflection

We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce next-level view-dependent effects—in real time. Unlike traditional MPI that uses a set of simple RGBα planes, our technique models view-dependent effects by instead parameterizing each pixel as a linear combination of basis functions learned from a neural network. Moreover, we propose a hybrid implicit-explicit modeling strategy that improves upon fine detail and produces state-of-the-art results. Our method is evaluated on benchmark forwardfacing datasets as well as our newly-introduced dataset designed to test the limit of view-dependent modeling with significantly more challenging effects such as the rainbow reflections on a CD. Our method achieves the best overall scores across all major metrics on these datasets with more than 1000× faster rendering time than the state of the art. For real-time demos, visit https://nex-mpi.github.io/

NeX: Real-time View Synthesis with Neural Basis Expansion

We present an image-based relighting method that can synthesize scene appearance under novel, distant illumination from the visible hemisphere, from only five images captured under pre-defined directional lights. Our method uses a deep convolutional neural network to regress the relit image from these five images; this relighting network is trained on a large synthetic dataset comprised of procedurally generated shapes with real-world reflectances. We show that by combining a custom-designed sampling network with the relighting network, we can jointly learn both the optimal input light directions and the relighting function. We present an extensive evaluation of our network, including an empirical analysis of reconstruction quality, optimal lighting configurations for different scenarios, and alternative network architectures. We demonstrate, on both synthetic and real scenes, that our method is able to reproduce complex, high-frequency lighting effects like specularities and cast shadows, and outperforms other image-based relighting methods that require an order of magnitude more images.

Deep image-based relighting from optimal sparse samples

The interaction of light and matter in the world surrounding us is of striking complexity and beauty. Since the very beginning of computer graphics, adequate modelling of these processes and efficient computation is an intensively studied research topic and still not a solved problem. The inherent complexity stems from the underlying physical processes as well as the global nature of the interactions that let light travel within a scene. This paper reviews the state of the art in interactive global illumination (GI) computation, i.e., methods that generate an image of a virtual scene in less than 1 s with an as exact as possible, or plausible, solution to the light transport. Additionally, the theoretical background and attempts to classify the broad field of methods are described. The strengths and weaknesses of different approaches, when applied to the different visual phenomena, arising from light interaction are compared and discussed. Finally, the paper concludes by highlighting design patterns for interactive GI and a list of open problems. © 2012 Wiley Periodicals, Inc.

https://cg.ivd.kit.edu/publications/2012/GISTAR/GISTAR.pdf

The State of the Art in Interactive Global Illumination

This paper introduces a new data representation and compression technique for precomputed radiance transfer (PRT). The light transfer functions and light sources are modeled with spherical radial basis functions (SRBFs). A SRBF is a rotation-invariant function that depends on the geodesic distance between two points on the unit sphere. Rotating functions in SRBF representation is as straightforward as rotating the centers of SRBFs. Moreover, high-frequency signals are handled by adjusting the bandwidth parameters of SRBFs. To exploit inter-vertex coherence, the light transfer functions are further classified iteratively into disjoint clusters, and tensor approximation is applied within each cluster. Compared with previous methods, the proposed approach enables real-time rendering with comparable quality under high-frequency lighting environments. The data storage is also more compact than previous all-frequency PRT algorithms.

https://ir.nctu.edu.tw:443/bitstream/11536/12079/1/000239817400058.pdf

All-frequency precomputed radiance transfer using spherical radial basis functions and clustered tensor approximation

We compress storage and accelerate performance of precomputed radiance transfer (PRT), which captures the way an object shadows, scatters, and reflects light. PRT records over many surface points a...

Clustered principal components for precomputed radiance transfer

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Clustered principal components for precomputed radiance transfer