MirrorNeRF: One-shot Neural Portrait RadianceField from Multi-mirror Catadioptric Imaging

• URL: http://arxiv.org/abs/2104.02607v1
• Date: Tue, 6 Apr 2021 15:48:47 GMT
• Title: MirrorNeRF: One-shot Neural Portrait RadianceField from Multi-mirror Catadioptric Imaging
• Authors: Ziyu Wang, Liao Wang, Fuqiang Zhao, Minye Wu, Lan Xu, Jingyi Yu
• Abstract summary: MirrorNeRF is a one-shot neural portrait free-viewpoint rendering approach using a catadioptric imaging system with multiple sphere mirrors and a single high-resolution digital camera. We introduce a novel neural radiance field representation to learn a continuous displacement field that implicitly compensates for the misalignment due to our flexible system setting.
• Score: 40.795365626968845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Photo-realistic neural reconstruction and rendering of the human portrait are critical for numerous VR/AR applications. Still, existing solutions inherently rely on multi-view capture settings, and the one-shot solution to get rid of the tedious multi-view synchronization and calibration remains extremely challenging. In this paper, we propose MirrorNeRF - a one-shot neural portrait free-viewpoint rendering approach using a catadioptric imaging system with multiple sphere mirrors and a single high-resolution digital camera, which is the first to combine neural radiance field with catadioptric imaging so as to enable one-shot photo-realistic human portrait reconstruction and rendering, in a low-cost and casual capture setting. More specifically, we propose a light-weight catadioptric system design with a sphere mirror array to enable diverse ray sampling in the continuous 3D space as well as an effective online calibration for the camera and the mirror array. Our catadioptric imaging system can be easily deployed with a low budget and the casual capture ability for convenient daily usages. We introduce a novel neural warping radiance field representation to learn a continuous displacement field that implicitly compensates for the misalignment due to our flexible system setting. We further propose a density regularization scheme to leverage the inherent geometry information from the catadioptric data in a self-supervision manner, which not only improves the training efficiency but also provides more effective density supervision for higher rendering quality. Extensive experiments demonstrate the effectiveness and robustness of our scheme to achieve one-shot photo-realistic and high-quality appearance free-viewpoint rendering for human portrait scenes.

Related papers

• SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream [26.165424006344267]
  Spike cameras offer distinct advantages over standard cameras. Existing approaches reliant on spike cameras often assume optimal illumination. We introduce SpikeNeRF, the first work that derives a NeRF-based volumetric scene representation from spike camera data.
  arXiv  Detail & Related papers  (2024-03-17T13:51:25Z)
• Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images [65.41966114373373]
  We present an improved solution to the neural image-based rendering problem in computer vision. The proposed approach could synthesize a realistic image of the scene from a novel viewpoint at test time.
  arXiv  Detail & Related papers  (2023-11-08T08:18:23Z)
• Robustifying the Multi-Scale Representation of Neural Radiance Fields [86.69338893753886]
  We present a robust multi-scale neural radiance fields representation approach to overcome both real-world imaging issues. Our method handles multi-scale imaging effects and camera-pose estimation problems with NeRF-inspired approaches. We demonstrate, with examples, that for an accurate neural representation of an object from day-to-day acquired multi-view images, it is crucial to have precise camera-pose estimates.
  arXiv  Detail & Related papers  (2022-10-09T11:46:45Z)
• VMRF: View Matching Neural Radiance Fields [57.93631771072756]
  VMRF is an innovative view matching NeRF that enables effective NeRF training without requiring prior knowledge in camera poses or camera pose distributions. VMRF introduces a view matching scheme, which exploits unbalanced optimal transport to produce a feature transport plan for mapping a rendered image with randomly camera pose to the corresponding real image. With the feature transport plan as the guidance, a novel pose calibration technique is designed which rectifies the initially randomized camera poses by predicting relative pose between the pair of rendered and real images.
  arXiv  Detail & Related papers  (2022-07-06T12:26:40Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• Single Image Automatic Radial Distortion Compensation Using Deep Convolutional Network [0.12891210250935145]
  We present a novel method for single-image automatic lens distortion compensation based on deep convolutional neural networks. The method is capable of real-time performance and accuracy using two highest-order coefficients of the radial distortion model operating in the application domain of sports broadcast.
  arXiv  Detail & Related papers  (2021-12-14T13:04:03Z)
• Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline [100.5353614588565]
  We propose to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-02T17:59:04Z)

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.