255,477 research outputs found
ImmersiveNeRF: Hybrid Radiance Fields for Unbounded Immersive Light Field Reconstruction
This paper proposes a hybrid radiance field representation for unbounded
immersive light field reconstruction which supports high-quality rendering and
aggressive view extrapolation. The key idea is to first formally separate the
foreground and the background and then adaptively balance learning of them
during the training process. To fulfill this goal, we represent the foreground
and background as two separate radiance fields with two different spatial
mapping strategies. We further propose an adaptive sampling strategy and a
segmentation regularizer for more clear segmentation and robust convergence.
Finally, we contribute a novel immersive light field dataset, named
THUImmersive, with the potential to achieve much larger space 6DoF immersive
rendering effects compared with existing datasets, by capturing multiple
neighboring viewpoints for the same scene, to stimulate the research and AR/VR
applications in the immersive light field domain. Extensive experiments
demonstrate the strong performance of our method for unbounded immersive light
field reconstruction
An interactive 3D medical visualization system based on a light field display
This paper presents a prototype medical data visualization system exploiting a light field display and custom direct volume rendering techniques to enhance understanding of massive volumetric data, such as CT, MRI, and PET scans. The system can be integrated with standard medical image archives and extends the capabilities of current radiology workstations by supporting real-time rendering of volumes of potentially unlimited size on light field displays generating dynamic observer-independent light fields. The system allows multiple untracked naked-eye users in a sufficiently large interaction area to coherently perceive rendered volumes as real objects, with stereo and motion parallax cues. In this way, an effective collaborative analysis of volumetric data can be achieved. Evaluation tests demonstrate the usefulness of the generated depth cues and the improved performance in understanding complex spatial structures with respect to standard techniques.883-893Pubblicat
Equivariant Light Field Convolution and Transformer
3D reconstruction and novel view rendering can greatly benefit from geometric
priors when the input views are not sufficient in terms of coverage and
inter-view baselines. Deep learning of geometric priors from 2D images often
requires each image to be represented in a canonical frame and the prior
to be learned in a given or learned canonical frame. In this paper, given
only the relative poses of the cameras, we show how to learn priors from
multiple views equivariant to coordinate frame transformations by proposing an
-equivariant convolution and transformer in the space of rays in 3D.
This enables the creation of a light field that remains equivariant to the
choice of coordinate frame. The light field as defined in our work, refers both
to the radiance field and the feature field defined on the ray space. We model
the ray space, the domain of the light field, as a homogeneous space of
and introduce the -equivariant convolution in ray space. Depending on
the output domain of the convolution, we present convolution-based
-equivariant maps from ray space to ray space and to . Our
mathematical framework allows us to go beyond convolution to
-equivariant attention in the ray space. We demonstrate how to tailor
and adapt the equivariant convolution and transformer in the tasks of
equivariant neural rendering and reconstruction from multiple views. We
demonstrate -equivariance by obtaining robust results in roto-translated
datasets without performing transformation augmentation.Comment: 46 page
Aleth-NeRF: Low-light Condition View Synthesis with Concealing Fields
Common capture low-light scenes are challenging for most computer vision
techniques, including Neural Radiance Fields (NeRF). Vanilla NeRF is
viewer-centred that simplifies the rendering process only as light emission
from 3D locations in the viewing direction, thus failing to model the
low-illumination induced darkness. Inspired by emission theory of ancient Greek
that visual perception is accomplished by rays casting from eyes, we make
slight modifications on vanilla NeRF to train on multiple views of low-light
scene, we can thus render out the well-lit scene in an unsupervised manner. We
introduce a surrogate concept, Concealing Fields, that reduce the transport of
light during the volume rendering stage. Specifically, our proposed method,
Aleth-NeRF, directly learns from the dark image to understand volumetric object
representation and concealing field under priors. By simply eliminating
Concealing Fields, we can render a single or multi-view well-lit image(s) and
gain superior performance over other 2D low light enhancement methods.
Additionally, we collect the first paired LOw-light and normal-light Multi-view
(LOM) datasets for future research.Comment: website page: https://cuiziteng.github.io/Aleth_NeRF_web
Recent results in rendering massive models on horizontal parallax-only light field displays
In this contribution, we report on specialized out-of-core multiresolution real-time rendering systems able to render massive surface and volume models on a special class of horizontal parallax-only light field displays. The displays are based on a specially arranged array of projectors emitting light beams onto a holographic screen, which then makes the necessary optical transformation to compose these beams into a continuous 3D view. The rendering methods employ state-of-the-art out-of-core multiresolution techniques able to correctly project geometries onto the display and to dynamically adapt model resolution by taking into account the particular spatial accuracy characteristics of the display. The programmability of latest generation graphics architectures is exploited to achieve interactive performance. As a result, multiple freely moving naked-eye viewers can inspect and manipulate virtual 3D objects that appear to them floating at fixed physical locations. The approach provides rapid visual understanding of complex multi-gigabyte surface models and volumetric data sets.304-30
Efficient View Synthesis with Neural Radiance Distribution Field
Recent work on Neural Radiance Fields (NeRF) has demonstrated significant
advances in high-quality view synthesis. A major limitation of NeRF is its low
rendering efficiency due to the need for multiple network forwardings to render
a single pixel. Existing methods to improve NeRF either reduce the number of
required samples or optimize the implementation to accelerate the network
forwarding. Despite these efforts, the problem of multiple sampling persists
due to the intrinsic representation of radiance fields. In contrast, Neural
Light Fields (NeLF) reduce the computation cost of NeRF by querying only one
single network forwarding per pixel. To achieve a close visual quality to NeRF,
existing NeLF methods require significantly larger network capacities which
limits their rendering efficiency in practice. In this work, we propose a new
representation called Neural Radiance Distribution Field (NeRDF) that targets
efficient view synthesis in real-time. Specifically, we use a small network
similar to NeRF while preserving the rendering speed with a single network
forwarding per pixel as in NeLF. The key is to model the radiance distribution
along each ray with frequency basis and predict frequency weights using the
network. Pixel values are then computed via volume rendering on radiance
distributions. Experiments show that our proposed method offers a better
trade-off among speed, quality, and network size than existing methods: we
achieve a ~254x speed-up over NeRF with similar network size, with only a
marginal performance decline. Our project page is at
yushuang-wu.github.io/NeRDF.Comment: Accepted by ICCV202
Research and Inhabited Image (RIA): a spatial hypothesis
This paper discusses the possibility of representing research activity as a narrative path starting from an experimentation field. The aim is to test and verify connections between social space and the construction of images of the world through the building and perception of specific language in the narrative dimension of research. The field work we present has been carried out as an installation art in Borromini’s Crypt in Rome, and is the example of rendering the story-dimension of research through a medium, a narrative technology in constant progress and evolution. In this way research activity can be presented as ascent and descent, as a mix of light and darkness, in multiple symbolic ways and different values. The Research and Inhabited Image Project (RIA) is in this dimension conceived as a story of stories, not as a research communication work
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
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