FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything (2403.00175v2)

Abstract: In the realm of computer vision, the integration of advanced techniques into the 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 map 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 \textit{RGB} and depth \textit{D} channels) interpretation of RGB-D data, facilitating the extraction of comprehensive and accurate object information. 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. The code and pre-trained models are publicly available at https://github.com/safouaneelg/FusionVision/.

• The paper introduces FusionVision, merging YOLO detection with FastSAM segmentation for accurate 3D object reconstruction from RGB-D data.
• It details a pipeline that integrates object detection, semantic segmentation, and point-cloud processing to address complex environmental challenges.
• Experimental results demonstrate high IoU and real-time performance (~27.3 FPS), highlighting potential for autonomous navigation and augmented reality applications.

FusionVision: A Comprehensive Approach to 3D Object Reconstruction and Segmentation Using RGB-D Cameras

The paper "FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras using YOLO and Fast Segment Anything (FastSAM)" introduces a robust pipeline aimed at the 3D segmentation of objects in RGB-D imagery. This research addresses the need for integrating advanced techniques in processing inputs from RGB-D cameras, overcoming the inherent challenges posed by varying environmental conditions and object appearances.

Traditional computer vision systems often fall short in simultaneously capturing precise object boundaries and achieving high detection accuracy on depth maps, as they are predominantly designed for RGB inputs. The authors propose FusionVision, an innovative system that integrates state-of-the-art object detection methodologies with advanced instance segmentation techniques. By merging YOLO, a well-established object detection model, with FastSAM, a high-performing semantic segmentation model, FusionVision facilitates a unified analysis of color and depth channels. This fusion provides a comprehensive interpretation of RGB-D data, enhancing processes like object localization, SLAM operations, and accurate dataset extraction.

Methodology

Data Acquisition and YOLO Training: The process begins with data collection and annotation to train the YOLO model. If the objects belong to the COCO dataset, pre-trained models can be used. Otherwise, custom data is needed, annotated using tools like Roboflow or LabelImg. The model's training is performed with augmented datasets to ensure robustness against various scenarios.

Model Inference and Segmentation: Once trained, the YOLO model is deployed on RGB images to detect objects. The detected bounding boxes serve as the input for FastSAM, facilitating refined segmentation masks via semantic segmentation. The combination of YOLO's detection prowess and FastSAM's segmentation capability delivers enhanced precision.

3D Object Reconstruction: The segmentation mask is aligned with the depth map from the RGB-D camera, leveraging intrinsic and extrinsic parameters for accurate 3D localization. The framework implements point-cloud processing, comprising downsampling and denoising steps, to ensure refined 3D object representations. This results in diminished noise, leading to accurate bounding box generation for each detected object.

Results and Implications

The proposed FusionVision pipeline achieves remarkable performance metrics: YOLO displays high intersection-over-union and precision across different conditions, with room for improvement in complex scenarios involving bottles. FastSAM consistently generates reliable segmentation masks, validated through comparative metrics such as IoU, Dice coefficient, and pixel-wise accuracy.

The improvements in processing time and frame rate, approximated at 27.3 FPS when all optimizations are applied, signify the pipeline's effectiveness for real-time applications. This performance underscores its potential for deployment in autonomous navigation, robotic systems, and augmented reality, where rapid and precise 3D object segmentation is crucial.

Conclusion

FusionVision represents a significant advancement in the integration of traditional 2D vision models with 3D data analysis for RGB-D cameras. By utilizing both YOLO and FastSAM, the approach introduces a novel framework yielding high accuracy in object detection and segmentation. The capability to adapt these models for 3D spatial understanding enhances several downstream applications, offering a comprehensive solution to the challenges posed by 3D object reconstruction and real-time segmentation.

Future research directions may explore incorporating LLMs to improve prompt-based specific object identification and leverage zero-shot detection capabilities to facilitate expansion into untrained categories and environments. This potential for further advancements highlights the evolving landscape of AI-assisted, real-world applications.