WaterNeRF: Neural Radiance Fields for Underwater Scenes
- URL: http://arxiv.org/abs/2209.13091v2
- Date: Fri, 29 Sep 2023 18:12:18 GMT
- Title: WaterNeRF: Neural Radiance Fields for Underwater Scenes
- Authors: Advaith Venkatramanan Sethuraman, Manikandasriram Srinivasan
Ramanagopal and Katherine A. Skinner
- Abstract summary: We advance state-of-the-art in neural radiance fields (NeRFs) to enable physics-informed dense depth estimation and color correction.
Our proposed method, WaterNeRF, estimates parameters of a physics-based model for underwater image formation.
We can produce novel views of degraded as well as corrected underwater images, along with dense depth of the scene.
- Score: 6.161668246821327
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Underwater imaging is a critical task performed by marine robots for a wide
range of applications including aquaculture, marine infrastructure inspection,
and environmental monitoring. However, water column effects, such as
attenuation and backscattering, drastically change the color and quality of
imagery captured underwater. Due to varying water conditions and
range-dependency of these effects, restoring underwater imagery is a
challenging problem. This impacts downstream perception tasks including depth
estimation and 3D reconstruction. In this paper, we advance state-of-the-art in
neural radiance fields (NeRFs) to enable physics-informed dense depth
estimation and color correction. Our proposed method, WaterNeRF, estimates
parameters of a physics-based model for underwater image formation, leading to
a hybrid data-driven and model-based solution. After determining the scene
structure and radiance field, we can produce novel views of degraded as well as
corrected underwater images, along with dense depth of the scene. We evaluate
the proposed method qualitatively and quantitatively on a real underwater
dataset.
Related papers
- NeuroPump: Simultaneous Geometric and Color Rectification for Underwater Images [52.863935209616635]
Underwater image restoration aims to remove geometric and color distortions due to water refraction, absorption and scattering.
We propose NeuroPump, a self-supervised method to simultaneously optimize and rectify underwater geometry and color as if water were pumped out.
arXiv Detail & Related papers (2024-12-20T13:40:28Z) - Underwater Image Quality Assessment: A Perceptual Framework Guided by Physical Imaging [52.860312888450096]
We propose a physically imaging-guided framework for underwater image quality assessment (UIQA) called PIGUIQA.
We incorporate advanced physics-based underwater imaging estimation into our method and define distortion metrics that measure the impact of direct transmission attenuation and backwards scattering on image quality.
PIGUIQA achieves state-of-the-art performance in underwater image quality prediction and exhibits strong generalizability.
arXiv Detail & Related papers (2024-12-20T03:31:45Z) - UW-SDF: Exploiting Hybrid Geometric Priors for Neural SDF Reconstruction from Underwater Multi-view Monocular Images [63.32490897641344]
We propose a framework for reconstructing target objects from multi-view underwater images based on neural SDF.
We introduce hybrid geometric priors to optimize the reconstruction process, markedly enhancing the quality and efficiency of neural SDF reconstruction.
arXiv Detail & Related papers (2024-10-10T16:33:56Z) - Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method [77.80712860663886]
4-D light fields (LFs) enhance underwater imaging plagued by light absorption, scattering, and other challenges.
We propose a progressive framework for underwater 4-D LF image enhancement and depth estimation.
We construct the first 4-D LF-based underwater image dataset for quantitative evaluation and supervised training of learning-based methods.
arXiv Detail & Related papers (2024-08-30T15:06:45Z) - UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation [5.596432047035205]
Underwater monocular depth estimation serves as the foundation for tasks such as 3D reconstruction of underwater scenes.
Existing methods fail to consider the unique characteristics of underwater environments.
In this paper, an end-to-end learning framework for underwater monocular depth estimation called UMono is presented.
arXiv Detail & Related papers (2024-07-25T07:52:11Z) - Beyond NeRF Underwater: Learning Neural Reflectance Fields for True
Color Correction of Marine Imagery [16.16700041031569]
Underwater imagery often exhibits distorted coloration as a result of light-water interactions.
We propose an algorithm to restore the true color (albedo) in underwater imagery by jointly learning the effects of the medium and neural scene representations.
arXiv Detail & Related papers (2023-04-06T21:29:34Z) - Seafloor-Invariant Caustics Removal from Underwater Imagery [0.0]
Caustics are complex physical phenomena resulting from the projection of light rays being refracted by the wavy surface.
In this work, we propose a novel method for correcting the effects of caustics on shallow underwater imagery.
In particular, the developed method employs deep learning architectures in order to classify image pixels to "non-caustics" and "caustics"
arXiv Detail & Related papers (2022-12-20T11:11:02Z) - Underwater Image Restoration via Contrastive Learning and a Real-world
Dataset [59.35766392100753]
We present a novel method for underwater image restoration based on unsupervised image-to-image translation framework.
Our proposed method leveraged contrastive learning and generative adversarial networks to maximize the mutual information between raw and restored images.
arXiv Detail & Related papers (2021-06-20T16:06:26Z) - Underwater image filtering: methods, datasets and evaluation [44.933577173776705]
We review the design principles of underwater image filtering methods.
We discuss image formation models and the results of restoration methods in various water types.
We present task-dependent enhancement methods and datasets for training neural networks and for method evaluation.
arXiv Detail & Related papers (2020-12-22T18:56:39Z) - Deep Sea Robotic Imaging Simulator [6.2122699483618]
The largest portion of the ocean - the deep sea - still remains mostly unexplored.
Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community.
This paper presents a physical model-based image simulation solution, which uses an in-air texture and depth information as inputs.
arXiv Detail & Related papers (2020-06-27T16:18:32Z) - Domain Adaptive Adversarial Learning Based on Physics Model Feedback for
Underwater Image Enhancement [10.143025577499039]
We propose a new robust adversarial learning framework via physics model based feedback control and domain adaptation mechanism for enhancing underwater images.
A new method for simulating underwater-like training dataset from RGB-D data by underwater image formation model is proposed.
Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method.
arXiv Detail & Related papers (2020-02-20T07:50:00Z)
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.