Optical Sensors and Methods for Underwater 3D Reconstruction

Massot-Campos, Miquel; Oliver-Codina, Gabriel

doi:10.3390/s151229864

Cited by 134 publications

(94 citation statements)

References 129 publications

(153 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our conclusion is consistent with the study by Starr and Lattimer [1]. Some specialized subsea LiDAR systems (please refer to Massot-Campos and Oliver-Codina [2] for a comprehensive survey of underwater 3D reconstruction), laser line scanning [3] or structured light [4,5,6,7] are able to operate in scattering media. However, these systems are power-consuming, slow, and bulky and thus they are not well suited for a walking robot.…”

Section: Introductionsupporting

confidence: 90%

Novel Descattering Approach for Stereo Vision in Dense Suspended Scatterer Environments

Nguyen

Park

Cho

et al. 2017

Sensors

View full text Add to dashboard Cite

In this paper, we propose a model-based scattering removal method for stereo vision for robot manipulation in indoor scattering media where the commonly used ranging sensors are unable to work. Stereo vision is an inherently ill-posed and challenging problem. It is even more difficult in the case of images of dense fog or dense steam scenes illuminated by active light sources. Images taken in such environments suffer attenuation of object radiance and scattering of the active light sources. To solve this problem, we first derive the imaging model for images taken in a dense scattering medium with a single active illumination close to the cameras. Based on this physical model, the non-uniform backscattering signal is efficiently removed. The descattered images are then utilized as the input images of stereo vision. The performance of the method is evaluated based on the quality of the depth map from stereo vision. We also demonstrate the effectiveness of the proposed method by carrying out the real robot manipulation task.

show abstract

Section: Introductionsupporting

confidence: 90%

Novel Descattering Approach for Stereo Vision in Dense Suspended Scatterer Environments

Nguyen

Park

Cho

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…An exhaustive survey on underwater 3D reconstruction methods can be found in [21]. The majority of approaches for underwater 3D reconstruction rely on standard SfM methods assuming the adapted pinhole camera model [12,16], however, this often leads to inaccurate 3D reconstruction [20].…”

Section: Prior Work On Underwater Sfmmentioning

confidence: 99%

Refractive Structure-from-Motion Through a Flat Refractive Interface

Chadebecq

Vasconcelos

Dwyer

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

View full text Add to dashboard Cite

show abstract

“…Active underwater depth estimation methods have been proposed in the past few years like the ones by Maccarone et al [1] and Bruno et al [2]. The article by Campos et al [16] gives an overview of different sensors and methods that have been used for underwater 3D. For single image depth estimation, the dark channel prior (DCP) [17] was one of the first methods to be used.…”

Section: Underwater Depth Estimationmentioning

confidence: 99%

Unsupervised Single Image Underwater Depth Estimation

Gupta

Mitra

2019

2019 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

Depth estimation from a single underwater image is one of the most challenging problems and is highly ill-posed. Due to the absence of large generalized underwater depth datasets and the difficulty in obtaining ground truth depth-maps, supervised learning techniques such as direct depth regression cannot be used. In this paper, we propose an unsupervised method for depth estimation from a single underwater image taken "in the wild" by using haze as a cue for depth. Our approach is based on indirect depth-map estimation where we learn the mapping functions between unpaired RGB-D terrestrial images and arbitrary underwater images to estimate the required depth-map. We propose a method which is based on the principles of cycle-consistent learning and uses dense-block based auto-encoders as generator networks. We evaluate and compare our method both quantitatively and qualitatively on various underwater images with diverse attenuation and scattering conditions and show that our method produces state-of-the-art results for unsupervised depth estimation from a single underwater image.

show abstract

Optical Sensors and Methods for Underwater 3D Reconstruction

Cited by 134 publications

References 129 publications

Novel Descattering Approach for Stereo Vision in Dense Suspended Scatterer Environments

Novel Descattering Approach for Stereo Vision in Dense Suspended Scatterer Environments

Refractive Structure-from-Motion Through a Flat Refractive Interface

Unsupervised Single Image Underwater Depth Estimation

Contact Info

Product

Resources

About