  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/.
