98 research outputs found
Neural Free-Viewpoint Relighting for Glossy Indirect Illumination
Precomputed Radiance Transfer (PRT) remains an attractive solution for
real-time rendering of complex light transport effects such as glossy global
illumination. After precomputation, we can relight the scene with new
environment maps while changing viewpoint in real-time. However, practical PRT
methods are usually limited to low-frequency spherical harmonic lighting.
All-frequency techniques using wavelets are promising but have so far had
little practical impact. The curse of dimensionality and much higher data
requirements have typically limited them to relighting with fixed view or only
direct lighting with triple product integrals. In this paper, we demonstrate a
hybrid neural-wavelet PRT solution to high-frequency indirect illumination,
including glossy reflection, for relighting with changing view. Specifically,
we seek to represent the light transport function in the Haar wavelet basis.
For global illumination, we learn the wavelet transport using a small
multi-layer perceptron (MLP) applied to a feature field as a function of
spatial location and wavelet index, with reflected direction and material
parameters being other MLP inputs. We optimize/learn the feature field
(compactly represented by a tensor decomposition) and MLP parameters from
multiple images of the scene under different lighting and viewing conditions.
We demonstrate real-time (512 x 512 at 24 FPS, 800 x 600 at 13 FPS) precomputed
rendering of challenging scenes involving view-dependent reflections and even
caustics.Comment: 13 pages, 9 figures, to appear in cgf proceedings of egsr 202
Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation
Inverse path tracing has recently been applied to joint material and lighting
estimation, given geometry and multi-view HDR observations of an indoor scene.
However, it has two major limitations: path tracing is expensive to compute,
and ambiguities exist between reflection and emission. Our Factorized Inverse
Path Tracing (FIPT) addresses these challenges by using a factored light
transport formulation and finds emitters driven by rendering errors. Our
algorithm enables accurate material and lighting optimization faster than
previous work, and is more effective at resolving ambiguities. The exhaustive
experiments on synthetic scenes show that our method (1) outperforms
state-of-the-art indoor inverse rendering and relighting methods particularly
in the presence of complex illumination effects; (2) speeds up inverse path
tracing optimization to less than an hour. We further demonstrate robustness to
noisy inputs through material and lighting estimates that allow plausible
relighting in a real scene. The source code is available at:
https://github.com/lwwu2/fiptComment: Updated experiment results; modified real-world section
Extending stochastic resonance for neuron models to general Levy noise
A recent paper by Patel and Kosko (2008) demonstrated stochastic resonance (SR) for general feedback continuous and spiking neuron models using additive Levy noise constrained to have finite second moments. In this brief, we drop this constraint and show that their result extends to general Levy noise models. We achieve this by showing that �¿large jump�¿ discontinuities in the noise can be controlled so as to allow the stochastic model to tend to a deterministic one as the noise dissipates to zero. SR then follows by a �¿forbidden intervals�¿ theorem as in Patel and Kosko's paper
Joint Material and Illumination Estimation from Photo Sets in the Wild
Faithful manipulation of shape, material, and illumination in 2D Internet
images would greatly benefit from a reliable factorization of appearance into
material (i.e., diffuse and specular) and illumination (i.e., environment
maps). On the one hand, current methods that produce very high fidelity
results, typically require controlled settings, expensive devices, or
significant manual effort. To the other hand, methods that are automatic and
work on 'in the wild' Internet images, often extract only low-frequency
lighting or diffuse materials. In this work, we propose to make use of a set of
photographs in order to jointly estimate the non-diffuse materials and sharp
lighting in an uncontrolled setting. Our key observation is that seeing
multiple instances of the same material under different illumination (i.e.,
environment), and different materials under the same illumination provide
valuable constraints that can be exploited to yield a high-quality solution
(i.e., specular materials and environment illumination) for all the observed
materials and environments. Similar constraints also arise when observing
multiple materials in a single environment, or a single material across
multiple environments. The core of this approach is an optimization procedure
that uses two neural networks that are trained on synthetic images to predict
good gradients in parametric space given observation of reflected light. We
evaluate our method on a range of synthetic and real examples to generate
high-quality estimates, qualitatively compare our results against
state-of-the-art alternatives via a user study, and demonstrate
photo-consistent image manipulation that is otherwise very challenging to
achieve
Capturing and Reconstructing the Appearance of Complex {3D} Scenes
In this thesis, we present our research on new acquisition methods for reflectance properties of real-world objects. Specifically, we first show a method for acquiring spatially varying densities in volumes of translucent, gaseous material with just a single image. This makes the method applicable to constantly changing phenomena like smoke without the use of high-speed camera equipment. Furthermore, we investigated how two well known techniques -- synthetic aperture confocal imaging and algorithmic descattering -- can be combined to help looking through a translucent medium like fog or murky water. We show that the depth at which we can still see an object embedded in the scattering medium is increased. In a related publication, we show how polarization and descattering based on phase-shifting can be combined for efficient 3D~scanning of translucent objects. Normally, subsurface scattering hinders the range estimation by offsetting the peak intensity beneath the surface away from the point of incidence. With our method, the subsurface scattering is reduced to a minimum and therefore reliable 3D~scanning is made possible. Finally, we present a system which recovers surface geometry, reflectance properties of opaque objects, and prevailing lighting conditions at the time of image capture from just a small number of input photographs. While there exist previous approaches to recover reflectance properties, our system is the first to work on images taken under almost arbitrary, changing lighting conditions. This enables us to use images we took from a community photo collection website
Relightable 3D Gaussian: Real-time Point Cloud Relighting with BRDF Decomposition and Ray Tracing
We present a novel differentiable point-based rendering framework for
material and lighting decomposition from multi-view images, enabling editing,
ray-tracing, and real-time relighting of the 3D point cloud. Specifically, a 3D
scene is represented as a set of relightable 3D Gaussian points, where each
point is additionally associated with a normal direction, BRDF parameters, and
incident lights from different directions. To achieve robust lighting
estimation, we further divide incident lights of each point into global and
local components, as well as view-dependent visibilities. The 3D scene is
optimized through the 3D Gaussian Splatting technique while BRDF and lighting
are decomposed by physically-based differentiable rendering. Moreover, we
introduce an innovative point-based ray-tracing approach based on the bounding
volume hierarchy for efficient visibility baking, enabling real-time rendering
and relighting of 3D Gaussian points with accurate shadow effects. Extensive
experiments demonstrate improved BRDF estimation and novel view rendering
results compared to state-of-the-art material estimation approaches. Our
framework showcases the potential to revolutionize the mesh-based graphics
pipeline with a relightable, traceable, and editable rendering pipeline solely
based on point cloud. Project
page:https://nju-3dv.github.io/projects/Relightable3DGaussian/
Lightweight Face Relighting
In this paper we present a method to relight human faces in real time, using consumer-grade graphics cards even with limited 3D capabilities. We show how to render faces using a combination of a simple, hardware-accelerated parametric model simulating skin shading and a detail texture map, and provide robust procedures to estimate all the necessary parameters for a given face. Our model strikes a balance between the difficulty of realistic face rendering (given the very specific reflectance properties of skin) and the goal of real-time rendering with limited hardware capabilities. This is accomplished by automatically generating an optimal set of parameters for a simple rendering model. We offer a discussion of the issues in face rendering to discern the pros and cons of various rendering models and to generalize our approach to most of the current hardware constraints. We provide results demonstrating the usability of our approach and the improvements we introduce both in the performance and in the visual quality of the resulting faces
OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects
We introduce OpenIllumination, a real-world dataset containing over 108K
images of 64 objects with diverse materials, captured under 72 camera views and
a large number of different illuminations. For each image in the dataset, we
provide accurate camera parameters, illumination ground truth, and foreground
segmentation masks. Our dataset enables the quantitative evaluation of most
inverse rendering and material decomposition methods for real objects. We
examine several state-of-the-art inverse rendering methods on our dataset and
compare their performances. The dataset and code can be found on the project
page: https://oppo-us-research.github.io/OpenIllumination
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