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/.

翻译：在计算机视觉领域，将先进技术集成至RGB-D相机输入处理流程中仍是一项重大挑战，其根源在于复杂环境条件与物体外观差异带来的固有复杂性。为此，本文提出FusionVision——一种面向RGB-D图像中物体鲁棒三维分割的完整流水线。传统计算机视觉系统主要针对RGB相机设计，在同时获取精确物体边界与深度图上的高精度目标检测方面存在局限。为应对这一挑战，FusionVision采用集成方法，将最先进的目标检测技术与先进的实例分割方法相融合。通过整合这些组件，实现对RGB-D数据的整体解读（即对来自彩色RGB通道与深度D通道的信息进行统一分析），从而提取全面且精确的物体信息。所提出的FusionVision流水线采用YOLO在RGB图像域内识别物体，随后应用创新性语义分割模型FastSAM勾勒物体边界，生成精细化分割掩码。各组件间的协同作用及其在三维场景理解中的集成，确保了目标检测与分割的一致性融合，大幅提升三维物体分割的整体精度。相关代码与预训练模型已开源发布于https://github.com/safouaneelg/FusionVision/。