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Cited by 24Advanced Neural Network ApplicationsAdvanced Vision and Imaging3D Shape Modeling and Analysis

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

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El Ghazouali, Safouane, Mhirit, Youssef, Oukhrid, Ali, Michelucci, Umberto, Nouira, Hichem·2024·Sensors
DOI 10.3390/s24092889PMC PMC11086350
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Abstract

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.

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Figures

Figure 1 Example of RGB-D camera scene capturing and 3D reconstruction. (a) 3D reconstruction from RGB-D depth-channel. (b) RGB stream capture from RGB sensor. (c) Visual estimation of depth with the ColorMap JET (the closer object are represented in green and far ones are the dark blue regions).
Figure 1 Example of RGB-D camera scene capturing and 3D reconstruction. (a) 3D reconstruction from RGB-D depth-channel. (b) RGB stream capture from RGB sensor. (c) Visual estimation of depth with the ColorMap JET (the closer object are represented in green and far ones are the dark blue regions).CC BY · PubMed Central
Figure 2 Complex YOLO framework for 3D object reconstruction and localization [47].
Figure 2 Complex YOLO framework for 3D object reconstruction and localization [47].CC BY · PubMed Central
Figure 3 Proposed FusionVision pipeline for real-time 3D object segmentation and localization using fused YOLO and FastSAM applied on RGB-D sensor.
Figure 3 Proposed FusionVision pipeline for real-time 3D object segmentation and localization using fused YOLO and FastSAM applied on RGB-D sensor.CC BY · PubMed Central
Figure 4 Visual representation of RGB camera alignment with the depth sensor.
Figure 4 Visual representation of RGB camera alignment with the depth sensor.CC BY · PubMed Central
Figure 5 Example of acquired images for YOLO training: the top two images are original, the bottom ones are augmented images.
Figure 5 Example of acquired images for YOLO training: the top two images are original, the bottom ones are augmented images.CC BY · PubMed Central
Figure 6 YOLO training curves: (a) bbox loss, (b) cls loss, (c) precision and recall, and (d) mAP50 and mAP50-95.
Figure 6 YOLO training curves: (a) bbox loss, (b) cls loss, (c) precision and recall, and (d) mAP50 and mAP50-95.CC BY · PubMed Central
Figure 7 Visuals of the YOLO detection, FastSAM mask extraction, and binary mask estimation: (a) using the pre-trained YOLO model; (b) using the custom trained YOLO model.
Figure 7 Visuals of the YOLO detection, FastSAM mask extraction, and binary mask estimation: (a) using the pre-trained YOLO model; (b) using the custom trained YOLO model.CC BY · PubMed Central
Figure 8 Overall evaluation metrics of FastSAM applied on extracted YOLO bounding boxes and compared to ground truth annotation. The blue points refers to the values of the metrics and black segments are standard deviations.
Figure 8 Overall evaluation metrics of FastSAM applied on extracted YOLO bounding boxes and compared to ground truth annotation. The blue points refers to the values of the metrics and black segments are standard deviations.CC BY · PubMed Central
Figure 9 Example of FastSAM misestimation of the segmentation mask: (a) original image, (b) ground truth annotation mask, and (c) FastSAM estimated mask.
Figure 9 Example of FastSAM misestimation of the segmentation mask: (a) original image, (b) ground truth annotation mask, and (c) FastSAM estimated mask.CC BY · PubMed Central
Figure 10 Three-dimensional object reconstruction from aligned FastSAM mask: (a) raw point-cloud and (b) post-processing point-cloud by voxel downsampling and statistical denoiser technique. The left images visualizing the YOLO detection, FastSAM mask extraction, and Binary mask estimation at specific positions of the physical objects within the frame.
Figure 10 Three-dimensional object reconstruction from aligned FastSAM mask: (a) raw point-cloud and (b) post-processing point-cloud by voxel downsampling and statistical denoiser technique. The left images visualizing the YOLO detection, FastSAM mask extraction, and Binary mask estimation at specific positions of the physical objects within the frame.CC BY · PubMed Central
Figure 11 Post-processing impact on 3D object reconstruction: (a) raw point-clouds, (b) Downsampled point-clouds, and (c) Downsampled + denoised point-clouds.
Figure 11 Post-processing impact on 3D object reconstruction: (a) raw point-clouds, (b) Downsampled point-clouds, and (c) Downsampled + denoised point-clouds.CC BY · PubMed Central

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Metadata

Authors
El Ghazouali, S., Mhirit, Y., Oukhrid, A., Michelucci, U., & Nouira, H.
Affiliations
  • TOELT LLC, AI Lab, 8406 Winterthur, Switzerland
  • Independent Researcher, 75000 Paris, France
  • Independent Researcher, 2502 Biel/Bienne, Switzerland
  • Laboratoire National de Métrologie et d'Essais (FR)
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2024-04-30
Volume
24
Issue
9
Pages
2889
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s24092889
PMC
PMC11086350
OpenAlex
W4392425776
Cited by
24
References
6
Citations by year
2026: 7 · 2025: 14 · 2024: 3
FWCI
3.36
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research) (cc-by)
Funders
  • EURAMET program (22DIT01-ViDiT, 23IND08-DiVision)
  • European Union’s Horizon 2020 research and innovation program (22DIT01-ViDiT, 23IND08-DiVision)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
Keywords
3D object reconstruction, 3D object segmentation, RGB-D camera, YOLO, instance segmentation, object detection, depth images, RGB-D fusion, 6D pose estimation, SLAM, 3D scene understanding

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CoChat. (2026). Citation check: FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras Using YOLO and Fast Segment Anything. https://app.cochat.ai/papers/10.3390/s24092889
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El Ghazouali, S., Mhirit, Y., Oukhrid, A., Michelucci, U., & Nouira, H. (2024). FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras Using YOLO and Fast Segment Anything. Sensors. https://doi.org/10.3390/s24092889