Snapshot 3D image projection using a diffractive decoder

• URL: http://arxiv.org/abs/2512.20464v1
• Date: Tue, 23 Dec 2025 15:57:08 GMT
• Title: Snapshot 3D image projection using a diffractive decoder
• Authors: Cagatay Isil, Alexander Chen, Yuhang Li, F. Onuralp Ardic, Shiqi Chen, Che-Yung Shen, Aydogan Ozcan,
• Abstract summary: We introduce a 3D display system comprising a digital encoder and a diffractive optical decoder.<n>The system achieves high-fidelity depth-resolved 3D image projection in a snapshot.<n>These results establish the diffractive 3D display system as a compact and scalable framework for depth-resolved snapshot 3D image projection.
• Score: 48.1381547559672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D image display is essential for next-generation volumetric imaging; however, dense depth multiplexing for 3D image projection remains challenging because diffraction-induced cross-talk rapidly increases as the axial image planes get closer. Here, we introduce a 3D display system comprising a digital encoder and a diffractive optical decoder, which simultaneously projects different images onto multiple target axial planes with high axial resolution. By leveraging multi-layer diffractive wavefront decoding and deep learning-based end-to-end optimization, the system achieves high-fidelity depth-resolved 3D image projection in a snapshot, enabling axial plane separations on the order of a wavelength. The digital encoder leverages a Fourier encoder network to capture multi-scale spatial and frequency-domain features from input images, integrates axial position encoding, and generates a unified phase representation that simultaneously encodes all images to be axially projected in a single snapshot through a jointly-optimized diffractive decoder. We characterized the impact of diffractive decoder depth, output diffraction efficiency, spatial light modulator resolution, and axial encoding density, revealing trade-offs that govern axial separation and 3D image projection quality. We further demonstrated the capability to display volumetric images containing 28 axial slices, as well as the ability to dynamically reconfigure the axial locations of the image planes, performed on demand. Finally, we experimentally validated the presented approach, demonstrating close agreement between the measured results and the target images. These results establish the diffractive 3D display system as a compact and scalable framework for depth-resolved snapshot 3D image projection, with potential applications in holographic displays, AR/VR interfaces, and volumetric optical computing.

Related papers

• FreqPDE: Rethinking Positional Depth Embedding for Multi-View 3D Object Detection Transformers [91.59069344768858]
  We introduce Frequency-aware Positional Depth Embedding (FreqPDE) to equip 2D image features with spatial information for 3D detection transformer decoder.<n>FreqPDE combines the 2D image features and 3D position embeddings to generate 3D depth-aware features for query decoding.
  arXiv  Detail & Related papers  (2025-10-17T07:36:54Z)
• Super-resolution image projection over an extended depth of field using a diffractive decoder [0.0]
  hybrid image projection system achieves extended depth-of-field with improved resolution.<n>System combines a convolutional neural network (CNN)-based digital encoder with an all-optical diffractive decoder.<n>Our pixel super-resolution (PSR) image projection system demonstrates high-fidelity image synthesis over an extended DOF of 267xW.
  arXiv  Detail & Related papers  (2025-10-04T20:42:57Z)
• PointVDP: Learning View-Dependent Projection by Fireworks Rays for 3D Point Cloud Segmentation [66.00721801098574]
  We propose view-dependent projection (VDP) to facilitate point cloud segmentation.<n>VDP generates data-driven projections from 3D point distributions.<n>We construct color regularization to optimize the framework.
  arXiv  Detail & Related papers  (2025-07-09T07:44:00Z)
• Stereo Image Coding for Machines with Joint Visual Feature Compression [69.28382442498408]
  The stereo image coding for machines (SICM) is formulated and explored in this paper.<n>A machine vision-oriented stereo feature compression network (MVSFC-Net) is proposed for SICM.<n>The proposed MVSFC-Net obtains superior compression efficiency as well as 3D visual task performance.
  arXiv  Detail & Related papers  (2025-02-20T01:46:17Z)
• Direct and Explicit 3D Generation from a Single Image [25.207277983430608]
  We introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D depth and RGB images. We incorporate epipolar attention into the latent-to-pixel decoder for pixel-level multi-view consistency. By back-projecting the generated depth pixels into 3D space, we create a structured 3D representation.
  arXiv  Detail & Related papers  (2024-11-17T03:14:50Z)
• GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision [49.839374549646884]
  This paper presents GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception.<n>Our approach achieves State-Of-The-Art performance on the Occ3D-nuScenes dataset with the least image resolution needed and the most weightless image backbone.
  arXiv  Detail & Related papers  (2024-05-17T07:31:20Z)
• Super-resolution image display using diffractive decoders [21.24387597787123]
  High-resolution synthesis/projection of images over a large field-of-view (FOV) is hindered by the restricted space-bandwidth-product (SBP) of wavefront modulators. We report a deep learning-enabled diffractive display design that is based on a jointly-trained pair of an electronic encoder and a diffractive optical decoder. Our results indicate that this diffractive image display can achieve a super-resolution factor of 4, demonstrating a 16-fold increase in SBP.
  arXiv  Detail & Related papers  (2022-06-15T03:42:36Z)
• 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)
• A New Comprehensive Framework for Multi-Exposure Stereo Coding Utilizing Low Rank Tucker-ALS and 3D-HEVC Techniques [5.6721325104114655]
  We propose an efficient scheme for coding multi-exposure stereo images based on a tensor low-rank approximation scheme.<n>The multi-exposure fusion can be realized to generate HDR stereo output at the decoder for increased realism and binocular 3D depth cues.<n>The encoding with 3D-HEVC enhance the proposed scheme efficiency by exploiting intra-frame, inter-view and the inter-component redundancies in lowrank approximated representation.
  arXiv  Detail & Related papers  (2021-04-10T10:10:14Z)

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.