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。