ArticleSensors (Basel, Switzerland)2024
FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras Using YOLO and Fast Segment Anything.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Semi-automated object-based image analysis for peri-urban land use/cover classification using WorldView-3 imagery of Mankweng, South Africa.Scientific reports · 2026Article
- Controlling an Industrial Robot Using Stereo 3D Vision Systems with AI Elements.Sensors (Basel, Switzerland) · 2025Article
- An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items.Sensors (Basel, Switzerland) · 2025Article
- DSOMF: A Dynamic Environment Simultaneous Localization and Mapping Technique Based on Machine Learning.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
Funding
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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
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Registered trials
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