Segmentation of three-dimensional (3D) point clouds is an important task for
autonomous systems. However, success of segmentation algorithms depends greatly
on the quality of the underlying point clouds (resolution, completeness etc.).
In particular, incomplete point clouds might reduce a downstream model's
performance. GRNet is proposed as a novel and recent deep learning solution to
complete point clouds, but it is not capable of part segmentation. On the other
hand, our proposed solution, GRJointNet, is an architecture that can perform
joint completion and segmentation on point clouds as a successor of GRNet.
Features extracted for the two tasks are also utilized by each other to
increase the overall performance. We evaluated our proposed network on the
ShapeNet-Part dataset and compared its performance to GRNet. Our results
demonstrate GRJointNet can outperform GRNet on point completion. It should also
be noted that GRNet is not capable of segmentation while GRJointNet is. This
study1, therefore, holds a promise to enhance practicality and utility of point
clouds in 3D vision for autonomous systems.

GRJointNet 是一个能够在不完整的 3D 点云上实现点云补全和分割的深度学习框架，能够显著提高自主系统中点云的实用性和效能。

GRJointNET：3D 不完整点云的协同完成和部分分割

GRJointNET: Synergistic Completion and Part Segmentation on 3D  Incomplete Point Clouds

Estimating the complete 3D point cloud from an incomplete one is a key
problem in many vision and robotics applications. Mainstream methods (e.g., PCN
and TopNet) use Multi-layer Perceptrons (MLPs) to directly process point
clouds, which may cause the loss of details because the structural and context
of point clouds are not fully considered. To solve this problem, we introduce
3D grids as intermediate representations to regularize unordered point clouds.
We therefore propose a novel Gridding Residual Network (GRNet) for point cloud
completion. In particular, we devise two novel differentiable layers, named
Gridding and Gridding Reverse, to convert between point clouds and 3D grids
without losing structural information. We also present the differentiable Cubic
Feature Sampling layer to extract features of neighboring points, which
preserves context information. In addition, we design a new loss function,
namely Gridding Loss, to calculate the L1 distance between the 3D grids of the
predicted and ground truth point clouds, which is helpful to recover details.
Experimental results indicate that the proposed GRNet performs favorably
against state-of-the-art methods on the ShapeNet, Completion3D, and KITTI
benchmarks.

本研究提出了一种名为 GRNet 的新方法，以 3D 网格为中间表示形式，通过保留结构和上下文信息来完善不完整的 3D 点云，并采用新的梯度损失函数来计算预测和真实点云之间的 L1 距离，实验结果表明这种方法在多个基准测试中表现优秀。