Neural Distortion Fields for Spatial Calibration of Wide Field-of-View Near-Eye Displays

• URL: http://arxiv.org/abs/2210.12389v1
• Date: Sat, 22 Oct 2022 08:48:31 GMT
• Title: Neural Distortion Fields for Spatial Calibration of Wide Field-of-View Near-Eye Displays
• Authors: Yuichi Hiroi, Kiyosato Someya, Yuta Itoh
• Abstract summary: We propose a calibration method for wide Field-of-View (FoV) Near-Eye Displays (NEDs) with complex image distortions. NDF is a fully connected deep neural network that implicitly represents display surfaces complexly distorted in spaces. NDF calibrates an augmented reality NED with 90$circ$ FoV with about 3.23 pixel (5.8 arcmin) median error using only 8 training viewpoints.
• Score: 7.683161309557347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a spatial calibration method for wide Field-of-View (FoV) Near-Eye Displays (NEDs) with complex image distortions. Image distortions in NEDs can destroy the reality of the virtual object and cause sickness. To achieve distortion-free images in NEDs, it is necessary to establish a pixel-by-pixel correspondence between the viewpoint and the displayed image. Designing compact and wide-FoV NEDs requires complex optical designs. In such designs, the displayed images are subject to gaze-contingent, non-linear geometric distortions, which explicit geometric models can be difficult to represent or computationally intensive to optimize. To solve these problems, we propose Neural Distortion Field (NDF), a fully-connected deep neural network that implicitly represents display surfaces complexly distorted in spaces. NDF takes spatial position and gaze direction as input and outputs the display pixel coordinate and its intensity as perceived in the input gaze direction. We synthesize the distortion map from a novel viewpoint by querying points on the ray from the viewpoint and computing a weighted sum to project output display coordinates into an image. Experiments showed that NDF calibrates an augmented reality NED with 90$^{\circ}$ FoV with about 3.23 pixel (5.8 arcmin) median error using only 8 training viewpoints. Additionally, we confirmed that NDF calibrates more accurately than the non-linear polynomial fitting, especially around the center of the FoV.

Related papers

• Möbius Transform for Mitigating Perspective Distortions in Representation Learning [43.86985901138407]
  Perspective distortion (PD) causes unprecedented changes in shape, size, orientation, angles, and other spatial relationships in images. We propose mitigating perspective distortion (MPD) by employing a fine-grained parameter control on a specific family of M"obius transform. We present a dedicated perspectively distorted benchmark dataset, ImageNet-PD, to benchmark the robustness of deep learning models against this new dataset.
  arXiv  Detail & Related papers  (2024-03-07T15:39:00Z)
• Text2Control3D: Controllable 3D Avatar Generation in Neural Radiance Fields using Geometry-Guided Text-to-Image Diffusion Model [39.64952340472541]
  We propose a controllable text-to-3D avatar generation method whose facial expression is controllable. Our main strategy is to construct the 3D avatar in Neural Radiance Fields (NeRF) optimized with a set of controlled viewpoint-aware images. We demonstrate the empirical results and discuss the effectiveness of our method.
  arXiv  Detail & Related papers  (2023-09-07T08:14:46Z)
• Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution [12.156009287223382]
  We analyze the coordinate transformation of the light field (LF) imaging process to reveal the geometric relationship in the LF images. We introduce a new LF subspace of virtual-slit images (VSI) that provide sub-pixel information complementary to sub-aperture images. To super-resolve image structures from undersampled LF data, we propose a geometry-aware decoder, named EPIXformer.
  arXiv  Detail & Related papers  (2023-05-31T16:27:00Z)
• RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning [62.86400614141706]
  We propose a new learning model, i.e., Rectangling Rectification Network (RecRecNet) Our model can flexibly warp the source structure to the target domain and achieves an end-to-end unsupervised deformation. Experiments show the superiority of our solution over the compared methods on both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2023-01-04T15:12:57Z)
• PVSeRF: Joint Pixel-, Voxel- and Surface-Aligned Radiance Field for Single-Image Novel View Synthesis [52.546998369121354]
  We present PVSeRF, a learning framework that reconstructs neural radiance fields from single-view RGB images. We propose to incorporate explicit geometry reasoning and combine it with pixel-aligned features for radiance field prediction. We show that the introduction of such geometry-aware features helps to achieve a better disentanglement between appearance and geometry.
  arXiv  Detail & Related papers  (2022-02-10T07:39:47Z)
• Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo [103.08512487830669]
  We present a modern solution to the multi-view photometric stereo problem (MVPS) We procure the surface orientation using a photometric stereo (PS) image formation model and blend it with a multi-view neural radiance field representation to recover the object's surface geometry. Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.
  arXiv  Detail & Related papers  (2021-10-11T20:20:03Z)
• Neural Étendue Expander for Ultra-Wide-Angle High-Fidelity Holographic Display [51.399291206537384]
  Modern holographic displays possess low 'etendue, which is the product of the display area and the maximum solid angle of diffracted light. We present neural 'etendue expanders, which are learned from a natural image dataset. With neural 'etendue expanders, we experimentally achieve 64$times$ 'etendue expansion of natural images in full color, expanding the FOV by an order of magnitude horizontally and vertically.
  arXiv  Detail & Related papers  (2021-09-16T17:21:52Z)
• Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering [60.02806355570514]
  Inferring representations of 3D scenes from 2D observations is a fundamental problem of computer graphics, computer vision, and artificial intelligence. We propose a novel neural scene representation, Light Field Networks or LFNs, which represent both geometry and appearance of the underlying 3D scene in a 360-degree, four-dimensional light field. Rendering a ray from an LFN requires only a *single* network evaluation, as opposed to hundreds of evaluations per ray for ray-marching or based on volumetrics.
  arXiv  Detail & Related papers  (2021-06-04T17:54:49Z)
• Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications [78.63280020581662]
  A novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views. We demonstrate the high performance and robustness of the proposed framework compared with state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-03-24T08:16:32Z)

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.