Masked signal modeling has greatly advanced self-supervised pre-training for
language and 2D images. However, it is still not fully explored in 3D scene
understanding. Thus, this paper introduces Masked Shape Prediction (MSP), a new
framework to conduct masked signal modeling in 3D scenes. MSP uses the
essential 3D semantic cue, i.e., geometric shape, as the prediction target for
masked points. The context-enhanced shape target consisting of explicit shape
context and implicit deep shape feature is proposed to facilitate exploiting
contextual cues in shape prediction. Meanwhile, the pre-training architecture
in MSP is carefully designed to alleviate the masked shape leakage from point
coordinates. Experiments on multiple 3D understanding tasks on both indoor and
outdoor datasets demonstrate the effectiveness of MSP in learning good feature
representations to consistently boost downstream performance.

本文介绍了 Masked Shape Prediction (MSP) 框架，它使用 3D 语义提示来预测掩码点，并引入了明确的形状上下文和隐式深度形状特征来帮助利用形状预测中的上下文提示。实验表明 MSP 可以提高下游任务的性能表现。

基于遮挡形状预测的自监督 3D 场景理解预训练

Self-supervised Pre-training with Masked Shape Prediction for 3D Scene  Understanding

3D point cloud completion, the task of inferring the complete geometric shape
from a partial point cloud, has been attracting attention in the community. For
acquiring high-fidelity dense point clouds and avoiding uneven distribution,
blurred details, or structural loss of existing methods' results, we propose a
novel approach to complete the partial point cloud in two stages. Specifically,
in the first stage, the approach predicts a complete but coarse-grained point
cloud with a collection of parametric surface elements. Then, in the second
stage, it merges the coarse-grained prediction with the input point cloud by a
novel sampling algorithm. Our method utilizes a joint loss function to guide
the distribution of the points. Extensive experiments verify the effectiveness
of our method and demonstrate that it outperforms the existing methods in both
the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).

本文提出了一种新的方法，通过两个阶段，利用参数化表面元素和采样算法预测粗粒度点云并与输入点云合并来完成部分点云。实验结果表明，该方法在 Earth Mover's Distance（EMD）和 Chamfer Distance（CD）等方面表现优异，优于现有方法。

稠密点云补全的变形采样网络

Morphing and Sampling Network for Dense Point Cloud Completion

Deep Learning for Geometric Shape Understating has organized a challenge for
extracting different kinds of skeletons from the images of different objects.
This competition is organized in association with CVPR 2019. There are three
different tracks of this competition. The present manuscript describes the
method used to train the model for the dataset provided in the first track. The
first track aims to extract skeleton pixels from the shape pixels of 89
different objects. For the purpose of extracting the skeleton, a U-net model
which is comprised of an encoder-decoder structure has been used. In our
proposed architecture, unlike the plain decoder in the traditional Unet, we
have designed the decoder in the format of HED architecture, wherein we have
introduced 4 side layers and fused them to one dilation convolutional layer to
connect the broken links of the skeleton. Our proposed architecture achieved
the F1 score of 0.77 on test data.

本文介绍了一种基于 U-net 模型和 HED 架构的方法，用于通过提取形状像素中的骨架像素来训练模型，以达到准确提取 89 种不同物体的骨架的目的，其 F1 分数达到了 0.77。