188 research outputs found
Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European
Conference on Computer Vision (ECCV) 2016;
https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5
Hardware and software improvements of volume splatting
This paper proposes different hardware-based acceleration of the three classical splatting strategies: emph{composite-every-sample}, emph{object-space sheet-buffer} and emph{image-space sheet-buffer}.Preprin
An Information-Theory Framework for Multi-Modal Visualization
The main goal of this master thesis is the development of new fusion strategies
that enhance multimodal visualization strategies
Time-varying volume visualization
Volume rendering is a very active research field in Computer Graphics because of its wide range of applications in various sciences, from medicine to flow mechanics. In this report, we survey a state-of-the-art on time-varying volume rendering. We state several basic concepts and then we establish several criteria to classify the studied works: IVR versus DVR, 4D versus 3D+time, compression techniques, involved architectures, use of parallelism and image-space versus object-space coherence. We also address other related problems as transfer functions and 2D cross-sections computation of time-varying volume data. All the papers reviewed are classified into several tables based on the mentioned classification and, finally, several conclusions are presented.Preprin
Flux-Limited Diffusion for Multiple Scattering in Participating Media
For the rendering of multiple scattering effects in participating media,
methods based on the diffusion approximation are an extremely efficient
alternative to Monte Carlo path tracing. However, in sufficiently transparent
regions, classical diffusion approximation suffers from non-physical radiative
fluxes which leads to a poor match to correct light transport. In particular,
this prevents the application of classical diffusion approximation to
heterogeneous media, where opaque material is embedded within transparent
regions. To address this limitation, we introduce flux-limited diffusion, a
technique from the astrophysics domain. This method provides a better
approximation to light transport than classical diffusion approximation,
particularly when applied to heterogeneous media, and hence broadens the
applicability of diffusion-based techniques. We provide an algorithm for
flux-limited diffusion, which is validated using the transport theory for a
point light source in an infinite homogeneous medium. We further demonstrate
that our implementation of flux-limited diffusion produces more accurate
renderings of multiple scattering in various heterogeneous datasets than
classical diffusion approximation, by comparing both methods to ground truth
renderings obtained via volumetric path tracing.Comment: Accepted in Computer Graphics Foru
SPARF: Large-Scale Learning of 3D Sparse Radiance Fields from Few Input Images
Recent advances in Neural Radiance Fields (NeRFs) treat the problem of novel
view synthesis as Sparse Radiance Field (SRF) optimization using sparse voxels
for efficient and fast rendering (plenoxels,InstantNGP). In order to leverage
machine learning and adoption of SRFs as a 3D representation, we present SPARF,
a large-scale ShapeNet-based synthetic dataset for novel view synthesis
consisting of 17 million images rendered from nearly 40,000 shapes at
high resolution (400 X 400 pixels). The dataset is orders of magnitude larger
than existing synthetic datasets for novel view synthesis and includes more
than one million 3D-optimized radiance fields with multiple voxel resolutions.
Furthermore, we propose a novel pipeline (SuRFNet) that learns to generate
sparse voxel radiance fields from only few views. This is done by using the
densely collected SPARF dataset and 3D sparse convolutions. SuRFNet employs
partial SRFs from few/one images and a specialized SRF loss to learn to
generate high-quality sparse voxel radiance fields that can be rendered from
novel views. Our approach achieves state-of-the-art results in the task of
unconstrained novel view synthesis based on few views on ShapeNet as compared
to recent baselines. The SPARF dataset will be made public with the code and
models on the project website https://abdullahamdi.com/sparf/ .Comment: Preprin
VMesh: Hybrid Volume-Mesh Representation for Efficient View Synthesis
With the emergence of neural radiance fields (NeRFs), view synthesis quality
has reached an unprecedented level. Compared to traditional mesh-based assets,
this volumetric representation is more powerful in expressing scene geometry
but inevitably suffers from high rendering costs and can hardly be involved in
further processes like editing, posing significant difficulties in combination
with the existing graphics pipeline. In this paper, we present a hybrid
volume-mesh representation, VMesh, which depicts an object with a textured mesh
along with an auxiliary sparse volume. VMesh retains the advantages of
mesh-based assets, such as efficient rendering, compact storage, and easy
editing, while also incorporating the ability to represent subtle geometric
structures provided by the volumetric counterpart. VMesh can be obtained from
multi-view images of an object and renders at 2K 60FPS on common consumer
devices with high fidelity, unleashing new opportunities for real-time
immersive applications.Comment: Project page: https://bennyguo.github.io/vmesh
Volumetric Medical Images Visualization on Mobile Devices
Volumetric medical images visualization is an important tool in the diagnosis
and treatment of diseases. Through history, one of the most dificult
tasks for Medicine Specialists has been the accurate location of broken bones
and of the damaged tissues during Chemotherapy treatment, among other
applications; like techniques used in Neurological Studies. Thus these situations
enhance the need of visualization in Medicine. New technologies,
the improvement and development of new hardware as well as software and
the updating of old ones for graphic applications have resulted in specialized
systems for medical visualization. However the use of these techniques
in mobile devices has been poor due to its low performance. In our work,
we propose a client-server scheme, where the model is compressed in the
server side and is reconstructed in a nal thin-client device. The technique
restricts the natural density values to achieve good bone visualization in
medical models, transforming the rest of the data to zero. Our proposal
uses a tridimensional Haar Wavelet Function locally applied inside units
blocks of 16x16x16, similar to the Wavelet Based 3D Compression Scheme
for Interactive Visualization of Very Large Volume Data approach. We also
implement a quantization algorithm which handles error coeficients according
to the frequency distributions of these coe cients. Finally, we made
an evaluation of the volume visualization; on current mobile devices .We
present the speci cations for the implementation of our technique in the
Nokia n900 Mobile Phone
HollowNeRF: Pruning Hashgrid-Based NeRFs with Trainable Collision Mitigation
Neural radiance fields (NeRF) have garnered significant attention, with
recent works such as Instant-NGP accelerating NeRF training and evaluation
through a combination of hashgrid-based positional encoding and neural
networks. However, effectively leveraging the spatial sparsity of 3D scenes
remains a challenge. To cull away unnecessary regions of the feature grid,
existing solutions rely on prior knowledge of object shape or periodically
estimate object shape during training by repeated model evaluations, which are
costly and wasteful.
To address this issue, we propose HollowNeRF, a novel compression solution
for hashgrid-based NeRF which automatically sparsifies the feature grid during
the training phase. Instead of directly compressing dense features, HollowNeRF
trains a coarse 3D saliency mask that guides efficient feature pruning, and
employs an alternating direction method of multipliers (ADMM) pruner to
sparsify the 3D saliency mask during training. By exploiting the sparsity in
the 3D scene to redistribute hash collisions, HollowNeRF improves rendering
quality while using a fraction of the parameters of comparable state-of-the-art
solutions, leading to a better cost-accuracy trade-off. Our method delivers
comparable rendering quality to Instant-NGP, while utilizing just 31% of the
parameters. In addition, our solution can achieve a PSNR accuracy gain of up to
1dB using only 56% of the parameters.Comment: Accepted to ICCV 202
Real-Time Terrain Storage Generation from Multiple Sensors towards Mobile Robot Operation Interface
A mobile robot mounted with multiple sensors is used to rapidly collect 3D point clouds and video images so as to allow accurate terrain modeling. In this study, we develop a real-time terrain storage generation and representation system including a nonground point database (PDB), ground mesh database (MDB), and texture database (TDB). A voxel-based flag map is proposed for incrementally registering large-scale point clouds in a terrain model in real time. We quantize the 3D point clouds into 3D grids of the flag map as a comparative table in order to remove the redundant points. We integrate the large-scale 3D point clouds into a nonground PDB and a node-based terrain mesh using the CPU. Subsequently, we program a graphics processing unit (GPU) to generate the TDB by mapping the triangles in the terrain mesh onto the captured video images. Finally, we produce a nonground voxel map and a ground textured mesh as a terrain reconstruction result. Our proposed methods were tested in an outdoor environment. Our results show that the proposed system was able to rapidly generate terrain storage and provide high resolution terrain representation for mobile mapping services and a graphical user interface between remote operators and mobile robots
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