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

In this article an innovative method for training regressive MLP networks is
presented, which is not subject to local minima. The Error-Back-Propagation
algorithm, proposed by William-Hinton-Rummelhart, has had the merit of
favouring the development of machine learning techniques, which has permeated
every branch of research and technology since the mid-1980s. This extraordinary
success is largely due to the black-box approach, but this same factor was also
seen as a limitation, as soon more challenging problems were approached. One of
the most critical aspects of the training algorithms was that of local minima
of the loss function, typically the mean squared error of the output on the
training set. In fact, as the most popular training algorithms are driven by
the derivatives of the loss function, there is no possibility to evaluate if a
reached minimum is local or global. The algorithm presented in this paper
avoids the problem of local minima, as the training is based on the properties
of the distribution of the training set, or better on its image internal to the
neural network. The performance of the algorithm is shown for a well-known
benchmark.

这篇文章介绍了一种创新的方法来训练回归 MLP 网络，该方法不受局部极小值的影响，通过基于训练集分布特性或更好地说是神经网络内部的图像来避免局部极小值问题，并通过一个著名的基准测试展示了算法的性能。

训练回归型 MLP 神经网络的免于局部最小值的算法

A free from local minima algorithm for training regressive MLP neural  networks

Multilayer-perceptrons (MLP) are known to struggle with learning functions of
high-frequencies, and in particular cases with wide frequency bands. We present
a spatially adaptive progressive encoding (SAPE) scheme for input signals of
MLP networks, which enables them to better fit a wide range of frequencies
without sacrificing training stability or requiring any domain specific
preprocessing. SAPE gradually unmasks signal components with increasing
frequencies as a function of time and space. The progressive exposure of
frequencies is monitored by a feedback loop throughout the neural optimization
process, allowing changes to propagate at different rates among local spatial
portions of the signal space. We demonstrate the advantage of SAPE on a variety
of domains and applications, including regression of low dimensional signals
and images, representation learning of occupancy networks, and a geometric task
of mesh transfer between 3D shapes.

本研究提出了一种空间自适应渐进编码方案，可以增强 MLP 网络对于高频率输入信号的适应能力，该编码方案能够在时间和空间上逐渐提高信号频率的曝光，并通过反馈循环监控进程中的频率暴露，以便信号空间的本地部分在不同的速率下传播变化，实验表明该方案在多个领域和应用中具有优势。