Point cloud registration is a fundamental task for estimating rigid
transformations between point clouds. Previous studies have used geometric
information for extracting features, matching and estimating transformation.
Recently, owing to the advancement of RGB-D sensors, researchers have attempted
to utilize visual information to improve registration performance. However,
these studies focused on extracting distinctive features by deep feature
fusion, which cannot effectively solve the negative effects of each feature's
weakness, and cannot sufficiently leverage the valid information. In this
paper, we propose a new feature combination framework, which applies a looser
but more effective fusion and can achieve better performance. An explicit
filter based on transformation consistency is designed for the combination
framework, which can overcome each feature's weakness. And an adaptive
threshold determined by the error distribution is proposed to extract more
valid information from the two types of features. Owing to the distinctive
design, our proposed framework can estimate more accurate correspondences and
is applicable to both hand-crafted and learning-based feature descriptors.
Experiments on ScanNet show that our method achieves a state-of-the-art
performance and the rotation accuracy of 99.1%.

通过提出一种新的特征组合框架，应用松散但更有效的融合，克服各种特征的弱点，并利用两种类型特征的有效信息，该方法在 ScanNet 上实现了最先进的性能，旋转精度达到了 99.1%。

RGBD-Glue: 强鲁棒性 RGB-D 点云配准的通用特征融合

RGBD-Glue: General Feature Combination for Robust RGB-D Point Cloud  Registration

Low-power event-driven computation and inherent temporal dynamics render
spiking neural networks (SNNs) ideal candidates for processing highly dynamic
and asynchronous signals from event-based sensors. However, due to the
challenges in training and architectural design constraints, there is a
scarcity of competitive demonstrations of SNNs in event-based dense prediction
compared to artificial neural networks (ANNs). In this work, we construct an
efficient spiking encoder-decoder network for large-scale event-based semantic
segmentation tasks, optimizing the encoder with hierarchical search. To improve
learning from highly dynamic event streams, we exploit the intrinsic adaptive
threshold of spiking neurons to modulate network activation. Additionally, we
develop a dual-path spiking spatially-adaptive modulation (SSAM) block to
enhance the representation of sparse events, significantly improving network
performance. Our network achieves 72.57% mean intersection over union (MIoU) on
the DDD17 dataset and 57.22% MIoU on the newly proposed larger DSEC-Semantic
dataset, surpassing current record ANNs by 4% while utilizing much lower
computation costs. To the best of our knowledge, this is the first instance of
SNNs outperforming ANNs in challenging event-based semantic segmentation tasks,
demonstrating their immense potential in event-based vision. Our code will be
publicly available.

本文提出了一种高效的 SNN 编码器 - 解码器网络，并结合层次搜索来优化编码器，从而实现对大规模事件信号的语义分割任务。此外，利用神经元固有的自适应阈值来调节网络激活，并开发了双通道刺激的空间自适应调制 (SSAM) 块，显著提高了网络性能，从而使得 SNN 在事件驱动的视觉任务上的表现优于 ANN 达到 72.57% 和 57.22% 的平均交并比 (MIoU)，并且具有更低的计算成本。

使用自适应脉冲编码器 - 解码器网络进行准确高效的基于事件的语义分割

Accurate and Efficient Event-based Semantic Segmentation Using Adaptive  Spiking Encoder-Decoder Network

Graph-based clustering methods like spectral clustering and SpectralNet are
very efficient in detecting clusters of non-convex shapes. Unlike the popular
$k$-means, graph-based clustering methods do not assume that each cluster has a
single mean. However, these methods need a graph where vertices in the same
cluster are connected by edges of large weights. To achieve this goal, many
studies have proposed graph reduction methods with parameters. Unfortunately,
these parameters have to be tuned for every dataset. We introduce a graph
reduction method that does not require any parameters. First, the distances
from every point $p$ to its neighbors are filtered using an adaptive threshold
to only keep neighbors with similar surrounding density. Second, the
similarities with close neighbors are computed and only high similarities are
kept. The edges that survive these two filtering steps form the constructed
graph that was passed to spectral clustering and SpectralNet. The experiments
showed that our method provides a stable alternative, where other methods
performance fluctuated according to the setting of their parameters.

本研究提出了一种不需要参数调整的图缩减方法来构建适用于谱聚类和 SpectralNet 的图，通过邻域密度和相似度过滤，实现了高效的聚类检测。