1,879 research outputs found
Path Guiding with Vertex Triplet Distributions
Good importance sampling strategies are decisive for the quality and robustness of photorealistic image synthesis with Monte Carlo integration. Path guiding approaches use transport paths sampled by an existing base sampler to build and refine a guiding distribution. This distribution then guides subsequent paths in regions that are otherwise hard to sample. We observe that all terms in the measurement contribution function sampled during path construction depend on at most three consecutive path vertices. We thus propose to build a 9D guiding distribution over vertex triplets that adapts to the full measurement contribution with a 9D Gaussian mixture model (GMM). For incremental path sampling, we query the model for the last two vertices of a path prefix, resulting in a 3D conditional distribution with which we sample the next vertex along the path. To make this approach scalable, we partition the scene with an octree and learn a local GMM for each leaf separately. In a learning phase, we sample paths using the current guiding distribution and collect triplets of path vertices. We resample these triplets online and keep only a fixed-size subset in reservoirs. After each progression, we obtain new GMMs from triplet samples by an initial hard clustering followed by expectation maximization. Since we model 3D vertex positions, our guiding distribution naturally extends to participating media. In addition, the symmetry in the GMM allows us to query it for paths constructed by a light tracer. Therefore our method can guide both a path tracer and light tracer from a jointly learned guiding distribution
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images
While numerous 3D reconstruction and novel-view synthesis methods allow for
photorealistic rendering of a scene from multi-view images easily captured with
consumer cameras, they bake illumination in their representations and fall
short of supporting advanced applications like material editing, relighting,
and virtual object insertion. The reconstruction of physically based material
properties and lighting via inverse rendering promises to enable such
applications.
However, most inverse rendering techniques require high dynamic range (HDR)
images as input, a setting that is inaccessible to most users. We present a
method that recovers the physically based material properties and
spatially-varying HDR lighting of a scene from multi-view, low-dynamic-range
(LDR) images. We model the LDR image formation process in our inverse rendering
pipeline and propose a novel optimization strategy for material, lighting, and
a camera response model. We evaluate our approach with synthetic and real
scenes compared to the state-of-the-art inverse rendering methods that take
either LDR or HDR input. Our method outperforms existing methods taking LDR
images as input, and allows for highly realistic relighting and object
insertion.Comment: Project Website: https://irisldr.github.io
Recent advances in transient imaging: A computer graphics and vision perspective
Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation
Recent advances in transient imaging: A computer graphics and vision perspective
Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation
Neural-PBIR Reconstruction of Shape, Material, and Illumination
Reconstructing the shape and spatially varying surface appearances of a
physical-world object as well as its surrounding illumination based on 2D
images (e.g., photographs) of the object has been a long-standing problem in
computer vision and graphics. In this paper, we introduce a robust object
reconstruction pipeline combining neural based object reconstruction and
physics-based inverse rendering (PBIR). Specifically, our pipeline firstly
leverages a neural stage to produce high-quality but potentially imperfect
predictions of object shape, reflectance, and illumination. Then, in the later
stage, initialized by the neural predictions, we perform PBIR to refine the
initial results and obtain the final high-quality reconstruction. Experimental
results demonstrate our pipeline significantly outperforms existing
reconstruction methods quality-wise and performance-wise
Distributionally Robust Optimization and Robust Statistics
We review distributionally robust optimization (DRO), a principled approach
for constructing statistical estimators that hedge against the impact of
deviations in the expected loss between the training and deployment
environments. Many well-known estimators in statistics and machine learning
(e.g. AdaBoost, LASSO, ridge regression, dropout training, etc.) are
distributionally robust in a precise sense. We hope that by discussing the DRO
interpretation of well-known estimators, statisticians who may not be too
familiar with DRO may find a way to access the DRO literature through the
bridge between classical results and their DRO equivalent formulation. On the
other hand, the topic of robustness in statistics has a rich tradition
associated with removing the impact of contamination. Thus, another objective
of this paper is to clarify the difference between DRO and classical
statistical robustness. As we will see, these are two fundamentally different
philosophies leading to completely different types of estimators. In DRO, the
statistician hedges against an environment shift that occurs after the decision
is made; thus DRO estimators tend to be pessimistic in an adversarial setting,
leading to a min-max type formulation. In classical robust statistics, the
statistician seeks to correct contamination that occurred before a decision is
made; thus robust statistical estimators tend to be optimistic leading to a
min-min type formulation
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