This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS),
with a focus on two significant issues in the state-of-the-art: foreground
leakage and sparse point distribution. The former arises from non-uniform point
sampling, allowing models to distinguish the density disparities between
foreground and background for easier segmentation. The latter results from
sampling only 2,048 points, limiting semantic information and deviating from
the real-world practice. To address these issues, we introduce a standardized
FS-PCS setting, upon which a new benchmark is built. Moreover, we propose a
novel FS-PCS model. While previous methods are based on feature optimization by
mainly refining support features to enhance prototypes, our method is based on
correlation optimization, referred to as Correlation Optimization Segmentation
(COSeg). Specifically, we compute Class-specific Multi-prototypical Correlation
(CMC) for each query point, representing its correlations to category
prototypes. Then, we propose the Hyper Correlation Augmentation (HCA) module to
enhance CMC. Furthermore, tackling the inherent property of few-shot training
to incur base susceptibility for models, we propose to learn non-parametric
prototypes for the base classes during training. The learned base prototypes
are used to calibrate correlations for the background class through a Base
Prototypes Calibration (BPC) module. Experiments on popular datasets
demonstrate the superiority of COSeg over existing methods. The code is
available at: this https URL

该研究论文通过引入标准化的少样本点云语义分割设置和基于相关性优化的新模型 (COSeg)，解决了目前该领域面临的前景泄漏、稀疏点分布等问题，在流行数据集上的实验证明了 COSeg 相对于现有方法的优越性。

重新思考少样本 3D 点云语义分割

Rethinking Few-shot 3D Point Cloud Semantic Segmentation

In this paper, a simultaneous localization and mapping (SLAM) method that
eliminates the influence of moving objects in dynamic environments is proposed.
This method utilizes the correlation between map points to separate points that
are part of the static scene and points that are part of different moving
objects into different groups. A sparse graph is first created using Delaunay
triangulation from all map points. In this graph, the vertices represent map
points, and each edge represents the correlation between adjacent points. If
the relative position between two points remains consistent over time, there is
correlation between them, and they are considered to be moving together
rigidly. If not, they are considered to have no correlation and to be in
separate groups. After the edges between the uncorrelated points are removed
during point-correlation optimization, the remaining graph separates the map
points of the moving objects from the map points of the static scene. The
largest group is assumed to be the group of reliable static map points.
Finally, motion estimation is performed using only these points. The proposed
method was implemented for RGB-D sensors, evaluated with a public RGB-D
benchmark, and tested in several additional challenging environments. The
experimental results demonstrate that robust and accurate performance can be
achieved by the proposed SLAM method in both slightly and highly dynamic
environments. Compared with other state-of-the-art methods, the proposed method
can provide competitive accuracy with good real-time performance.

本文提出了一种同时定位与地图构建方法，能够在动态环境下消除移动物体的影响，该方法利用地图点之间的相关性将静态场景点与移动物体点分组，并依此进行运动估计，实验证明该方法能在动态环境下保证鲁棒和准确性。