Cutting Voxel Projector a New Approach to Construct 3D Cone Beam CT
Operator
- URL: http://arxiv.org/abs/2110.09841v1
- Date: Tue, 19 Oct 2021 10:54:01 GMT
- Title: Cutting Voxel Projector a New Approach to Construct 3D Cone Beam CT
Operator
- Authors: Vojt\v{e}ch Kulvait (1), Georg Rose (1) ((1) Institute for Medical
Engineering and Research Campus STIMULATE, University of Magdeburg,
Magdeburg, Germany)
- Abstract summary: We introduce a new class of projectors for 3D cone beam tomographic reconstruction.
We use analytical formulas for the relationship between the voxel volume projected onto a given detector pixel and its contribution to the extinction value detected on that pixel.
We construct a near-exact projector and backprojector that can be used especially for algebraic reconstruction techniques.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In this paper, we introduce a new class of projectors for 3D cone beam
tomographic reconstruction. We find analytical formulas for the relationship
between the voxel volume projected onto a given detector pixel and its
contribution to the extinction value detected on that pixel. Using this
approach, we construct a near-exact projector and backprojector that can be
used especially for algebraic reconstruction techniques. We have implemented
this cutting voxel projector and a less accurate, speed-optimized version of it
together with two established projectors, a ray tracing projector based on
Siddon's algorithm and a TT footprint projector. We show that the cutting voxel
projector achieves, especially for large cone beam angles, noticeably higher
accuracy than the TT projector. Moreover, our implementation of the relaxed
version of the cutting voxel projector is significantly faster than current
footprint projector implementations. We further show that Siddon's algorithm
with comparable accuracy would be much slower than the cutting voxel projector.
All algorithms are implemented within an open source framework for algebraic
reconstruction in OpenCL 1.2 and C++ and are optimized for GPU computation.
They are published as open-source software under the GNU GPL 3 license, see
https://github.com/kulvait/KCT_cbct.
Related papers
- 3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes [50.36933474990516]
This work considers ray tracing the particles, building a bounding volume hierarchy and casting a ray for each pixel using high-performance ray tracing hardware.
To efficiently handle large numbers of semi-transparent particles, we describe a specialized algorithm which encapsulates particles with bounding meshes.
Experiments demonstrate the speed and accuracy of our approach, as well as several applications in computer graphics and vision.
arXiv Detail & Related papers (2024-07-09T17:59:30Z) - CompenHR: Efficient Full Compensation for High-resolution Projector [68.42060996280064]
Full projector compensation is a practical task of projector-camera systems.
It aims to find a projector input image, named compensation image, such that when projected it cancels the geometric and photometric distortions.
State-of-the-art methods use deep learning to address this problem and show promising performance for low-resolution setups.
However, directly applying deep learning to high-resolution setups is impractical due to the long training time and high memory cost.
arXiv Detail & Related papers (2023-11-22T14:13:27Z) - Differentiable Forward Projector for X-ray Computed Tomography [6.1868857343691115]
Data-driven deep learning has been successfully applied to various computed tomographic reconstruction problems.
This paper presents an accurate differentiable forward and back projection software library to ensure the consistency between the predicted images and the original measurements.
arXiv Detail & Related papers (2023-07-11T20:52:46Z) - Neural Projection Mapping Using Reflectance Fields [11.74757574153076]
We introduce a projector into a neural reflectance field that allows to calibrate the projector and photo realistic light editing.
Our neural field consists of three neural networks, estimating geometry, material, and transmittance.
We believe that neural projection mapping opens up the door to novel and exciting downstream tasks, through the joint optimization of the scene and projection images.
arXiv Detail & Related papers (2023-06-11T05:33:10Z) - Extracting Triangular 3D Models, Materials, and Lighting From Images [59.33666140713829]
We present an efficient method for joint optimization of materials and lighting from multi-view image observations.
We leverage meshes with spatially-varying materials and environment that can be deployed in any traditional graphics engine.
arXiv Detail & Related papers (2021-11-24T13:58:20Z) - Directionally Decomposing Structured Light for Projector Calibration [22.062182997296805]
Intrinsic projector calibration is essential in projection mapping (PM) applications.
We present a practical calibration device that requires a minimal working volume directly in front of the projector lens.
We demonstrate that our technique can calibrate projectors with different focusing distances and aperture sizes at the same accuracy as a conventional method.
arXiv Detail & Related papers (2021-10-08T06:44:01Z) - End-to-end Full Projector Compensation [81.19324259967742]
Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface.
In this paper, we propose the first end-to-end differentiable solution, named CompenNeSt++, to solve the two problems jointly.
arXiv Detail & Related papers (2020-07-30T18:23:52Z) - Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled
Representation [57.11299763566534]
We present a solution to recover 3D pose from multi-view images captured with spatially calibrated cameras.
We exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points.
Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections.
arXiv Detail & Related papers (2020-04-05T12:52:29Z) - Learning to Accelerate Decomposition for Multi-Directional 3D Printing [31.658049974100088]
Multi-directional 3D printing has the capability of decreasing or eliminating the need for support structures.
Recent work proposed a beam-guided search algorithm to find an optimized sequence of plane-clipping.
We propose a learning framework that can accelerate the beam-guided search by using a smaller number of the original beam width.
arXiv Detail & Related papers (2020-03-17T18:37:44Z) - DeProCams: Simultaneous Relighting, Compensation and Shape
Reconstruction for Projector-Camera Systems [91.45207885902786]
We propose a novel end-to-end trainable model named DeProCams to learn the photometric and geometric mappings of ProCams.
DeProCams explicitly decomposes the projector-camera image mappings into three subprocesses: shading attributes estimation, rough direct light estimation and photorealistic neural rendering.
In our experiments, DeProCams shows clear advantages over previous arts with promising quality and being fully differentiable.
arXiv Detail & Related papers (2020-03-06T05:49:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.