Directionally Decomposing Structured Light for Projector Calibration

• URL: http://arxiv.org/abs/2110.03924v1
• Date: Fri, 8 Oct 2021 06:44:01 GMT
• Title: Directionally Decomposing Structured Light for Projector Calibration
• Authors: Masatoki Sugimoto, Daisuke Iwai, Koki Ishida, Parinya Punpongsanon, Kosuke Sato
• Abstract summary: 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.
• Score: 22.062182997296805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intrinsic projector calibration is essential in projection mapping (PM) applications, especially in dynamic PM. However, due to the shallow depth-of-field (DOF) of a projector, more work is needed to ensure accurate calibration. We aim to estimate the intrinsic parameters of a projector while avoiding the limitation of shallow DOF. As the core of our technique, we present a practical calibration device that requires a minimal working volume directly in front of the projector lens regardless of the projector's focusing distance and aperture size. The device consists of a flat-bed scanner and pinhole-array masks. For calibration, a projector projects a series of structured light patterns in the device. The pinholes directionally decompose the structured light, and only the projected rays that pass through the pinholes hit the scanner plane. For each pinhole, we extract a ray passing through the optical center of the projector. Consequently, we regard the projector as a pinhole projector that projects the extracted rays only, and we calibrate the projector by applying the standard camera calibration technique, which assumes a pinhole camera model. Using a proof-of-concept prototype, we demonstrate that our technique can calibrate projectors with different focusing distances and aperture sizes at the same accuracy as a conventional method. Finally, we confirm that our technique can provide intrinsic parameters accurate enough for a dynamic PM application, even when a projector is placed too far from a projection target for a conventional method to calibrate the projector using a fiducial object of reasonable size.

Related papers

• A Note on Geometric Calibration of Multiple Cameras and Projectors [1.693200946453174]
  We discuss several important components of a real-world geometric calibration procedure used in our laboratory. We specifically discuss the design of the calibration object and the image processing pipeline used to analyze it in the acquired images.
  arXiv  Detail & Related papers  (2024-10-24T07:54:49Z)
• RETINA: a hardware-in-the-loop optical facility with reduced optical aberrations [0.0]
  Vision-based navigation algorithms have established themselves as effective solutions to determine the spacecraft state in orbit with low-cost and versatile sensors. A dedicated simulation framework must be developed to emulate the orbital environment in a laboratory setup. This paper presents the design of a low-aberration optical facility called RETINA to perform this task.
  arXiv  Detail & Related papers  (2024-07-02T11:26:37Z)
• 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)
• 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)
• Cutting Voxel Projector a New Approach to Construct 3D Cone Beam CT Operator [0.10923877073891444]
  We introduce a novel class of projectors for 3D cone beam tomographic reconstruction. Our method enables local refinement of voxels, allowing for adaptive grid resolution and improved reconstruction quality. Results demonstrate that the cutting voxel projector achieves higher accuracy than the TT projector, especially for large cone beam angles.
  arXiv  Detail & Related papers  (2021-10-19T10:54:01Z)
• How to Calibrate Your Event Camera [58.80418612800161]
  We propose a generic event camera calibration framework using image reconstruction. We show that neural-network-based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras.
  arXiv  Detail & Related papers  (2021-05-26T07:06:58Z)
• GMLight: Lighting Estimation via Geometric Distribution Approximation [86.95367898017358]
  This paper presents a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation. We parameterize illumination scenes in terms of the geometric light distribution, light intensity, ambient term, and auxiliary depth, and estimate them as a pure regression task. With the estimated lighting parameters, the generative projector synthesizes panoramic illumination maps with realistic appearance and frequency.
  arXiv  Detail & Related papers  (2021-02-20T03:31:52Z)
• SPAA: Stealthy Projector-based Adversarial Attacks on Deep Image Classifiers [82.19722134082645]
  A stealthy projector-based adversarial attack is proposed in this paper. We approximate the real project-and-capture operation using a deep neural network named PCNet. Our experiments show that the proposed SPAA clearly outperforms other methods by achieving higher attack success rates.
  arXiv  Detail & Related papers  (2020-12-10T18:14:03Z)
• 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)
• 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.