Although recent point cloud analysis achieves impressive progress, the
paradigm of representation learning from a single modality gradually meets its
bottleneck. In this work, we take a step towards more discriminative 3D point
cloud representation by fully taking advantages of images which inherently
contain richer appearance information, e.g., texture, color, and shade.
Specifically, this paper introduces a simple but effective point cloud
cross-modality training (PointCMT) strategy, which utilizes view-images, i.e.,
rendered or projected 2D images of the 3D object, to boost point cloud
analysis. In practice, to effectively acquire auxiliary knowledge from view
images, we develop a teacher-student framework and formulate the cross modal
learning as a knowledge distillation problem. PointCMT eliminates the
distribution discrepancy between different modalities through novel feature and
classifier enhancement criteria and avoids potential negative transfer
effectively. Note that PointCMT effectively improves the point-only
representation without architecture modification. Sufficient experiments verify
significant gains on various datasets using appealing backbones, i.e., equipped
with PointCMT, PointNet++ and PointMLP achieve state-of-the-art performance on
two benchmarks, i.e., 94.4% and 86.7% accuracy on ModelNet40 and ScanObjectNN,
respectively. Code will be made available at
this https URL

本文介绍了一种称为 PointCMT 的简单而有效的点云交叉模态训练策略，通过使用视图图像（即三维对象的渲染或投影的二维图像）来促进点云分析，进而更好地实现了三维点云表征。 实验证明，PointCMT 有效地改进了点云的表示方法，在不修改架构的情况下，基于 PointCMT 的模型在 ModelNet40 和 ScanObjectNN 等各种数据集上均实现了最先进的性能。

利用图像获取更多信息：点云跨模态训练用于形状分析

Let Images Give You More:Point Cloud Cross-Modal Training for Shape  Analysis

This paper proposes a self-supervised learning approach for video features
that results in significantly improved performance on downstream tasks (such as
video classification, captioning and segmentation) compared to existing
methods. Our method extends the BERT model for text sequences to the case of
sequences of real-valued feature vectors, by replacing the softmax loss with
noise contrastive estimation (NCE). We also show how to learn representations
from sequences of visual features and sequences of words derived from ASR
(automatic speech recognition), and show that such cross-modal training (when
possible) helps even more.

本文提出了一种自监督学习方法，用于视频特征，与现有方法相比在下游任务中表现显著提高，通过将文本序列的 BERT 模型扩展到实值特征向量序列的情况，以噪声对比估计取代 softma 损失函数，同时展示了如何从可视特征序列和 ASR 派生的词序列中学习表示，并且表明这样的跨模态训练可以进一步提高性能。