667 research outputs found
A halo bias function measured deeply into voids without stochasticity
We study the relationship between dark-matter haloes and matter in the MIP
-body simulation ensemble, which allows precision measurements of this
relationship, even deeply into voids. What enables this is a lack of
discreteness, stochasticity, and exclusion, achieved by averaging over hundreds
of possible sets of initial small-scale modes, while holding fixed large-scale
modes that give the cosmic web. We find (i) that dark-matter-halo formation is
greatly suppressed in voids; there is an exponential downturn at low densities
in the otherwise power-law matter-to-halo density bias function. Thus, the
rarity of haloes in voids is akin to the rarity of the largest clusters, and
their abundance is quite sensitive to cosmological parameters. The exponential
downturn appears both in an excursion-set model, and in a model in which
fluctuations evolve in voids as in an open universe with an effective
proportional to a large-scale density. We also find that (ii) haloes
typically populate the average halo-density field in a super-Poisson way, i.e.
with a variance exceeding the mean; and (iii) the rank-order-Gaussianized halo
and dark-matter fields are impressively similar in Fourier space. We compare
both their power spectra and cross-correlation, supporting the conclusion that
one is roughly a strictly-increasing mapping of the other. The MIP ensemble
especially reveals how halo abundance varies with `environmental' quantities
beyond the local matter density; (iv) we find a visual suggestion that at fixed
matter density, filaments are more populated by haloes than clusters.Comment: Changed to version accepted by MNRA
SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections
In this work, we present SceneDreamer, an unconditional generative model for
unbounded 3D scenes, which synthesizes large-scale 3D landscapes from random
noise. Our framework is learned from in-the-wild 2D image collections only,
without any 3D annotations. At the core of SceneDreamer is a principled
learning paradigm comprising 1) an efficient yet expressive 3D scene
representation, 2) a generative scene parameterization, and 3) an effective
renderer that can leverage the knowledge from 2D images. Our approach begins
with an efficient bird's-eye-view (BEV) representation generated from simplex
noise, which includes a height field for surface elevation and a semantic field
for detailed scene semantics. This BEV scene representation enables 1)
representing a 3D scene with quadratic complexity, 2) disentangled geometry and
semantics, and 3) efficient training. Moreover, we propose a novel generative
neural hash grid to parameterize the latent space based on 3D positions and
scene semantics, aiming to encode generalizable features across various scenes.
Lastly, a neural volumetric renderer, learned from 2D image collections through
adversarial training, is employed to produce photorealistic images. Extensive
experiments demonstrate the effectiveness of SceneDreamer and superiority over
state-of-the-art methods in generating vivid yet diverse unbounded 3D worlds.Comment: Project Page https://scene-dreamer.github.io/ Code
https://github.com/FrozenBurning/SceneDreame
Modelling and Visualisation of the Optical Properties of Cloth
Cloth and garment visualisations are widely used in fashion and interior design, entertaining, automotive and nautical industry and are indispensable elements of visual communication. Modern appearance models attempt to offer a complete solution for the visualisation of complex cloth properties. In the review part of the chapter, advanced methods that enable visualisation at micron resolution, methods used in three-dimensional (3D) visualisation workflow and methods used for research purposes are presented. Within the review, those methods offering a comprehensive approach and experiments on explicit clothes attributes that present specific optical phenomenon are analysed. The review of appearance models includes surface and image-based models, volumetric and explicit models. Each group is presented with the representative authors’ research group and the application and limitations of the methods. In the final part of the chapter, the visualisation of cloth specularity and porosity with an uneven surface is studied. The study and visualisation was performed using image data obtained with photography. The acquisition of structure information on a large scale namely enables the recording of structure irregularities that are very common on historical textiles, laces and also on artistic and experimental pieces of cloth. The contribution ends with the presentation of cloth visualised with the use of specular and alpha maps, which is the result of the image processing workflow
Invisible Seams
International audienceSurface materials are commonly described by attributes stored in textures (for instance, color, normal, or displacement). Interpolation during texture lookup provides a continuous value field everywhere on the surface, except at the chart boundaries where visible discontinuities appear. We propose a solution to make these seams invisible, while still outputting a standard texture atlas. Our method relies on recent advances in quad remeshing using global parameterization to produce a set of texture coordinates aligning texel grids across chart boundaries. This property makes it possible to ensure that the interpolated value fields on both sides of a chart boundary precisely match, making all seams invisible. However, this requirement on the uv coordinates needs to be complemented by a set of constraints on the colors stored in the texels. We propose an algorithm solving for all the necessary constraints between texel values, including through different magnification modes (nearest, bilinear, biquadratic and bicubic), and across facets using different texture resolutions. In the typical case of bilinear magnification and uniform resolution, none of the texels appearing on the surface are constrained. Our approach also ensures perfect continuity across several MIP-mapping levels
360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360 Radiance Fields
Virtual tour among sparse 360 images is widely used while hindering
smooth and immersive roaming experiences. The emergence of Neural Radiance
Field (NeRF) has showcased significant progress in synthesizing novel views,
unlocking the potential for immersive scene exploration. Nevertheless, previous
NeRF works primarily focused on object-centric scenarios, resulting in
noticeable performance degradation when applied to outward-facing and
large-scale scenes due to limitations in scene parameterization. To achieve
seamless and real-time indoor roaming, we propose a novel approach using
geometry-aware radiance fields with adaptively assigned local radiance fields.
Initially, we employ multiple 360 images of an indoor scene to
progressively reconstruct explicit geometry in the form of a probabilistic
occupancy map, derived from a global omnidirectional radiance field.
Subsequently, we assign local radiance fields through an adaptive
divide-and-conquer strategy based on the recovered geometry. By incorporating
geometry-aware sampling and decomposition of the global radiance field, our
system effectively utilizes positional encoding and compact neural networks to
enhance rendering quality and speed. Additionally, the extracted floorplan of
the scene aids in providing visual guidance, contributing to a realistic
roaming experience. To demonstrate the effectiveness of our system, we curated
a diverse dataset of 360 images encompassing various real-life scenes,
on which we conducted extensive experiments. Quantitative and qualitative
comparisons against baseline approaches illustrated the superior performance of
our system in large-scale indoor scene roaming
Improving Filtering for Computer Graphics
When drawing images onto a computer screen, the information in the scene is typically
more detailed than can be displayed. Most objects, however, will not be close to the
camera, so details have to be filtered out, or anti-aliased, when the objects are drawn on
the screen. I describe new methods for filtering images and shapes with high fidelity while
using computational resources as efficiently as possible.
Vector graphics are everywhere, from drawing 3D polygons to 2D text and maps for
navigation software. Because of its numerous applications, having a fast, high-quality
rasterizer is important. I developed a method for analytically rasterizing shapes using
wavelets. This approach allows me to produce accurate 2D rasterizations of images and
3D voxelizations of objects, which is the first step in 3D printing. I later improved my
method to handle more filters. The resulting algorithm creates higher-quality images than
commercial software such as Adobe Acrobat and is several times faster than the most
highly optimized commercial products.
The quality of texture filtering also has a dramatic impact on the quality of a rendered
image. Textures are images that are applied to 3D surfaces, which typically cannot be
mapped to the 2D space of an image without introducing distortions. For situations in
which it is impossible to change the rendering pipeline, I developed a method for precomputing
image filters over 3D surfaces. If I can also change the pipeline, I show that it
is possible to improve the quality of texture sampling significantly in real-time rendering
while using the same memory bandwidth as used in traditional methods
PyNeRF: Pyramidal Neural Radiance Fields
Neural Radiance Fields (NeRFs) can be dramatically accelerated by spatial
grid representations. However, they do not explicitly reason about scale and so
introduce aliasing artifacts when reconstructing scenes captured at different
camera distances. Mip-NeRF and its extensions propose scale-aware renderers
that project volumetric frustums rather than point samples but such approaches
rely on positional encodings that are not readily compatible with grid methods.
We propose a simple modification to grid-based models by training model heads
at different spatial grid resolutions. At render time, we simply use coarser
grids to render samples that cover larger volumes. Our method can be easily
applied to existing accelerated NeRF methods and significantly improves
rendering quality (reducing error rates by 20-90% across synthetic and
unbounded real-world scenes) while incurring minimal performance overhead (as
each model head is quick to evaluate). Compared to Mip-NeRF, we reduce error
rates by 20% while training over 60x faster.Comment: Neurips 2023 Project page: https://haithemturki.com/pynerf
- …