基于RGB-D室内数据深度学习的3D实例分割
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data
AI总结：
针对室内场景下3D实例分割研究不足的问题，提出融合深度信息的改进Mask R-CNN方法，通过2D分割结果与深度图结合生成3D点云，实验验证其可有效支撑智能系统与机器人的目标操作。
摘要：
3D目标识别是工业与家庭室内环境下智能系统和机器人系统面临的一项挑战性任务。这类系统能否识别并分割出其频繁遇到的3D目标实例至关重要，该问题已得到计算机视觉、图形学和机器学习领域的广泛关注。传统方法依赖手工设计的特征和定制方案实现3D分割，不仅性能未达预期，也无法推广到大规模数据场景。近年来，深度学习方法凭借在2D计算机视觉领域的巨大成功，已成为解决3D分割难题的主流方案，但目前实例分割方向的相关研究仍较为有限。\n本文提出一种基于深度学习、利用红绿蓝深度（RGB-D）数据实现高效3D实例分割的新方法。我们对带有点渲染模块的2D区域卷积神经网络（Mask R-CNN）深度学习模型进行适配，使其融合深度信息，以识别并分割目标的3D实例。为生成3D点云坐标(x, y, z)，我们将RGB图像中已识别目标区域的分割后2D像素(u, v)，与深度图像中的(u, v)点进行合并。此外，我们还开展了实验与分析，从不同视角和距离维度对所提方法进行对比。实验结果表明，所提出的3D目标识别与实例分割方法能够有效支撑机器人系统与智能系统中的目标操作任务。
英文摘要：
3D object recognition is a challenging task for intelligent and robot systems in industrial and home indoor environments. It is critical for such systems to recognize and segment the 3D object instances that they encounter on a frequent basis. The computer vision, graphics, and machine learning fields have all given it a lot of attention. Traditionally, 3D segmentation was done with hand-crafted features and designed approaches that did not achieve acceptable performance and could not be generalized to large-scale data. Deep learning approaches have lately become the preferred method for 3D segmentation challenges by their great success in 2D computer vision. However, the task of instance segmentation is currently less explored. In this paper, we propose a novel approach for efficient 3D instance segmentation using red green blue and depth (RGB-D) data based on deep learning. The 2D region based convolutional neural networks (Mask R-CNN) deep learning model with point based rending module is adapted to integrate with depth information to recognize and segment 3D instances of objects. In order to generate 3D point cloud coordinates (x, y, z), segmented 2D pixels (u, v) of recognized object regions in the RGB image are merged into (u, v) points of the depth image. Moreover, we conducted an experiment and analysis to compare our proposed method from various points of view and distances. The experimentation shows the proposed 3D object recognition and instance segmentation are sufficiently beneficial to support object handling in robotic and intelligent systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
