In the field of deep point cloud understanding, KPConv is a unique
architecture that uses kernel points to locate convolutional weights in space,
instead of relying on Multi-Layer Perceptron (MLP) encodings. While it
initially achieved success, it has since been surpassed by recent MLP networks
that employ updated designs and training strategies. Building upon the kernel
point principle, we present two novel designs: KPConvD (depthwise KPConv), a
lighter design that enables the use of deeper architectures, and KPConvX, an
innovative design that scales the depthwise convolutional weights of KPConvD
with kernel attention values. Using KPConvX with a modern architecture and
training strategy, we are able to outperform current state-of-the-art
approaches on the ScanObjectNN, Scannetv2, and S3DIS datasets. We validate our
design choices through ablation studies and release our code and models.

通过使用核心点而不是多层感知器编码来定位卷积权重的方法，KPConvD 和 KPConvX 以更深的架构和核心注意力值进行了创新的设计，实现了更好的性能，超越了最新的 MLP 网络设计和训练策略。我们通过消融研究验证了我们设计选择的有效性，并发布了我们的代码和模型。

KPConvX：利用核心注意力现代化核心点卷积

KPConvX: Modernizing Kernel Point Convolution with Kernel Attention

Deep learning techniques for point cloud data have demonstrated great
potentials in solving classical problems in 3D computer vision such as 3D
object classification and segmentation. Several recent 3D object classification
methods have reported state-of-the-art performance on CAD model datasets such
as ModelNet40 with high accuracy (~92%). Despite such impressive results, in
this paper, we argue that object classification is still a challenging task
when objects are framed with real-world settings. To prove this, we introduce
ScanObjectNN, a new real-world point cloud object dataset based on scanned
indoor scene data. From our comprehensive benchmark, we show that our dataset
poses great challenges to existing point cloud classification techniques as
objects from real-world scans are often cluttered with background and/or are
partial due to occlusions. We identify three key open problems for point cloud
object classification, and propose new point cloud classification neural
networks that achieve state-of-the-art performance on classifying objects with
cluttered background. Our dataset and code are publicly available in our
project page this https URL

本文研究基于点云数据的深度学习技术在解决三维计算机视觉领域中的经典问题方面的应用。作者介绍了 ScanObjectNN 数据集并展示了其对于点云对象分类问题的挑战性，提出了三个关键的挑战问题，并提出了一些解决方案，最终实现了在背景杂乱的情况下对于点云对象的分类，达到了最佳效果。