Accuracy of RGB-D camera-based and stereophotogrammetric facial scanners: a comparative study

Pan, Fangwei; Liu, Jialing; Cen, Yueyan; Chen, Ye; Cai, Ruilie; Liao, Wen; Wang, Jian

doi:10.1016/j.jdent.2022.104302

Cited by 9 publications

(2 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior studies have only evaluated the dimensional accuracy by measuring displacement of rendered models to a reference. 38,39 However, measuring differences in linear distance between rendered and reference models is limited to interpreting 3D volumetric changes from a 2D imaging plane. Therefore, we quantitatively measured volumes to objectively assess dimensional and morphological accuracy.…”

Section: Validation Of Automated Stereophotogrammetrymentioning

confidence: 99%

Optimization of a Novel Automated, Low Cost, Three-Dimensional Photogrammetry System (PHACE)

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

IntroductionClinical tools are neither standardized nor ubiquitous to monitor volumetric or morphological changes in the periorbital region and ocular adnexa due to pathology such as oculofacial trauma, thyroid eye disease, and the natural aging process. We have developed a low-cost, three dimensionally printedPHotogrammetry forAutomatedCarE(PHACE) system to evaluate three-dimensional (3D) measurements of periocular and adnexal tissue.MethodsThe PHACE system uses two Google Pixel 3 smartphones attached to automatic rotating platforms to image a subject’s face through a cutout board patterned with registration marks. Photographs of faces were taken from many perspectives by the cameras placed on the rotating platform. Faces were imaged with and without 3D printed hemispheric phantom lesions (black domes) affixed on the forehead above the brow. Images were rendered into 3D models in Metashape (Agisoft, St. Petersburg, Russia) and then processed and analyzed in CloudCompare (CC) and Autodesk’s Meshmixer. The 3D printed hemispheres affixed to the face were then quantified within Meshmixer and compared to their known volumes. Finally, we compared digital exophthalmometry measurements with results from a standard Hertel exophthalmometer in a subject with and without an orbital prosthesis.ResultsQuantification of 3D printed phantom volumes using optimized stereophotogrammetry demonstrated a 2.5% error for a 244μL phantom, and 7.6% error for a 27.5μL phantom. Digital exophthalmometry measurements differed by 0.72mm from a standard exophthalmometer.ConclusionWe demonstrated an optimized workflow using our custom apparatus to analyze and quantify oculofacial volumetric and dimensions changes with a resolution of 244μL. This apparatus is a low-cost tool that can be used in clinical settings to objectively monitor volumetric and morphological changes in periorbital anatomy.

show abstract

Section: Validation Of Automated Stereophotogrammetrymentioning

confidence: 99%

Optimization of a Novel Automated, Low Cost, Three-Dimensional Photogrammetry System (PHACE)

Wang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These cameras use infrared (IR) projectors and sensors to measure the distance of objects from the camera, providing an additional depth dimension to the RGB image with sufficient accuracy. For example, according to F. Pan et al [35], an estimated accuracy of 0.61 ± 0.42 mm has been assessed on RGB-D camera for facial scanning. Compared to traditional RGB cameras, RGB-D cameras offer several advantages, including:…”

Section: Introductionmentioning

confidence: 99%

FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras Using YOLO and Fast Segment Anything

El Ghazouali,

Mhirit,

Oukhrid

et al. 2024

Sensors

View full text Add to dashboard Cite

In the realm of computer vision, the integration of advanced techniques into the pre-processing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmentation of objects in RGB-D imagery. Traditional computer vision systems face limitations in simultaneously capturing precise object boundaries and achieving high-precision object detection on depth maps, as they are mainly proposed for RGB cameras. To address this challenge, FusionVision adopts an integrated approach by merging state-of-the-art object detection techniques, with advanced instance segmentation methods. The integration of these components enables a holistic (unified analysis of information obtained from both color RGB and depth D channels) interpretation of RGB-D data, facilitating the extraction of comprehensive and accurate object information in order to improve post-processes such as object 6D pose estimation, Simultanious Localization and Mapping (SLAM) operations, accurate 3D dataset extraction, etc. The proposed FusionVision pipeline employs YOLO for identifying objects within the RGB image domain. Subsequently, FastSAM, an innovative semantic segmentation model, is applied to delineate object boundaries, yielding refined segmentation masks. The synergy between these components and their integration into 3D scene understanding ensures a cohesive fusion of object detection and segmentation, enhancing overall precision in 3D object segmentation.

show abstract

Perception in Complex and Unstructured Infrastructure Environments

Huang,

Pan,

Dissanayake

2023

Infrastructure Robotics

View full text Add to dashboard Cite

Accuracy of RGB-D camera-based and stereophotogrammetric facial scanners: a comparative study

Cited by 9 publications

References 50 publications

Optimization of a Novel Automated, Low Cost, Three-Dimensional Photogrammetry System (PHACE)

Optimization of a Novel Automated, Low Cost, Three-Dimensional Photogrammetry System (PHACE)

FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras Using YOLO and Fast Segment Anything

Perception in Complex and Unstructured Infrastructure Environments

Contact Info

Product

Resources

About