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.

通过将深度学习与 RGB-D 数据相结合，提出了一种用于高效 3D 实例分割的新方法，该方法通过点状渲染模块将 2D 区域卷积神经网络（Mask R-CNN）模型与深度信息集成，可以识别和分割对象的 3D 实例，实验证明该方法对于支持机器人和智能系统中的物体处理非常有益。