Color Pass-Through via Camera-Display Coupling

• URL: http://arxiv.org/abs/2607.12746v1
• Date: Tue, 14 Jul 2026 13:16:10 GMT
• Title: Color Pass-Through via Camera-Display Coupling
• Abstract summary: We propose Color Pass-Through, an end-to-end learned framework that operates directly on captured images.<n> Coupling the camera and display yields two practical advantages.<n>Our method achieves an average gain of +2.0 points on a 5-point user study and more than 2x improvement on quantitative metrics.
• Score: 17.070341956620176
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: When a real-world scene is captured by a smartphone camera and viewed on its screen, the displayed image often differs noticeably from the original scene in color, brightness, and contrast. This gap persists despite substantial advances in both modern cameras and displays. A key reason is that most pipelines factor the high-dimensional capture-to-display process into two separately calibrated camera and display stages, and then connect them through low-dimensional color transforms, leading to information bottlenecks and inevitable error accumulation. To address this systemic challenge, we propose Color Pass-Through, an end-to-end learned framework that operates directly on captured images. Our key insight is to treat the camera and display as a coupled system rather than calibrating them in isolation. Coupling the camera and display yields two practical advantages: (1) it brings the entire real-world scenes to the display via end-to-end optimization, and (2) it allows efficient one-step calibration for each distinct observer via complete capture-to-display path. We validate Color Pass-Through using both digital and human observers. Compared with representative baselines, our method achieves an average gain of +2.0 points on a 5-point user study and more than 2x improvement on quantitative metrics, demonstrating improved reproduction of the perceived color of the original scene.

Related papers

• Two-Stage Camera Calibration Method for Multi-Camera Systems Using Scene Geometry [52.27010615715154]
  This paper presents a novel two-stage calibration method for multi-camera tracking systems.<n>In the first stage, partial calibration of individual cameras is performed based on an operator's annotation of natural geometric primitives.<n>In the second stage, precise system calibration is achieved through interactive manipulation of the projected Effective Field of View.
  arXiv  Detail & Related papers  (2025-12-04T16:47:39Z)
• M2SVid: End-to-End Inpainting and Refinement for Monocular-to-Stereo Video Conversion [60.728003408015844]
  We propose a novel architecture for inpainting and refinement of the warped right view obtained by depth-based reprojection of the input left view.<n>Our approach outperforms previous state-of-the-art methods, obtaining an average rank of 1.43 among the 4 compared methods in a user study.
  arXiv  Detail & Related papers  (2025-05-22T11:58:54Z)
• Real-Time Computational Visual Aberration Correcting Display Through High-Contrast Inverse Blurring [0.0]
  We develop a live vision-correcting display (VCD) to address refractive visual aberrations without the need for glasses or contact lenses.<n>We achieve this correction through deconvolution of the displayed image using a point spread function (PSF) associated with the viewer's eye.<n>The results of our display demonstrate significant improvements in visual clarity, achieving a structural similarity index (SSIM) of 83.04%.
  arXiv  Detail & Related papers  (2024-12-30T11:15:45Z)
• EF-3DGS: Event-Aided Free-Trajectory 3D Gaussian Splatting [87.1077910795879]
  Event cameras, inspired by biological vision, record pixel-wise intensity changes asynchronously with high temporal resolution.<n>We propose Event-Aided Free-Trajectory 3DGS, which seamlessly integrates the advantages of event cameras into 3DGS.<n>We evaluate our method on the public Tanks and Temples benchmark and a newly collected real-world dataset, RealEv-DAVIS.
  arXiv  Detail & Related papers  (2024-10-20T13:44:24Z)
• Neuromorphic Synergy for Video Binarization [54.195375576583864]
  Bimodal objects serve as a visual form to embed information that can be easily recognized by vision systems. Neuromorphic cameras offer new capabilities for alleviating motion blur, but it is non-trivial to first de-blur and then binarize the images in a real-time manner. We propose an event-based binary reconstruction method that leverages the prior knowledge of the bimodal target's properties to perform inference independently in both event space and image space. We also develop an efficient integration method to propagate this binary image to high frame rate binary video.
  arXiv  Detail & Related papers  (2024-02-20T01:43:51Z)
• DVANet: Disentangling View and Action Features for Multi-View Action Recognition [56.283944756315066]
  We present a novel approach to multi-view action recognition where we guide learned action representations to be separated from view-relevant information in a video. Our model and method of training significantly outperforms all other uni-modal models on four multi-view action recognition datasets.
  arXiv  Detail & Related papers  (2023-12-10T01:19:48Z)
• Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration [66.33746403815283]
  We propose a scene-adaptive infrared and visible image registration. We employ homography to simulate the deformation between different planes. We propose the first ground truth available misaligned infrared and visible image dataset.
  arXiv  Detail & Related papers  (2023-04-12T06:49:56Z)
• Cross-Camera Deep Colorization [10.254243409261898]
  We propose an end-to-end convolutional neural network to align and fuse images from a color-plus-mono dual-camera system. Our method consistently achieves substantial improvements, i.e., around 10dB PSNR gain.
  arXiv  Detail & Related papers  (2022-08-26T11:02:14Z)
• Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images [10.091921099426294]
  We explore an alternative approach to splicing detection, which is potentially better suited for images in-the-wild. We learn a deep metric space that is on one hand sensitive to illumination color and camera white-point estimation, but on the other hand insensitive to variations in object color. In our evaluation, we show that the proposed embedding space outperforms the state of the art on images that have been subject to strong compression and downsampling.
  arXiv  Detail & Related papers  (2022-06-21T21:28:40Z)
• Jointly Optimizing Color Rendition and In-Camera Backgrounds in an RGB Virtual Production Stage [2.2304863069891834]
  LED panels used in virtual production systems produce problematic color shifts when used as lighting due to their peaky spectral output from narrow-band red, green, and blue LEDs. We present an improved color calibration process for virtual production stages which ameliorates this color rendition problem while also passing through accurate in-camera background colors.
  arXiv  Detail & Related papers  (2022-05-24T23:23:40Z)
• Colour alignment for relative colour constancy via non-standard references [11.92389176996629]
  Relative colour constancy is an essential requirement for many scientific imaging applications. We propose a colour alignment model that considers the camera image formation as a black-box. It formulates colour alignment as a three-step process: camera response calibration, response linearisation, and colour matching.
  arXiv  Detail & Related papers  (2021-12-30T15:58:55Z)

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.