Given the prominence of current 3D sensors, a fine-grained analysis on the
basic point cloud data is worthy of further investigation. Particularly, real
point cloud scenes can intuitively capture complex surroundings in the real
world, but due to 3D data's raw nature, it is very challenging for machine
perception. In this work, we concentrate on the essential visual task, semantic
segmentation, for large-scale point cloud data collected in reality. On the one
hand, to reduce the ambiguity in nearby points, we augment their local context
by fully utilizing both geometric and semantic features in a bilateral
structure. On the other hand, we comprehensively interpret the distinctness of
the points from multiple resolutions and represent the feature map following an
adaptive fusion method at point-level for accurate semantic segmentation.
Further, we provide specific ablation studies and intuitive visualizations to
validate our key modules. By comparing with state-of-the-art networks on three
different benchmarks, we demonstrate the effectiveness of our network.

该研究关注于针对实际采集的大规模点云数据进行语义分割，并提出了一种自适应融合方法在点水平上进行准确的分割。

通过双边增强和自适应融合进行真实点云场景的语义分割

Semantic Segmentation for Real Point Cloud Scenes via Bilateral  Augmentation and Adaptive Fusion

Hyperspectral cameras can provide unique spectral signatures for consistently
distinguishing materials that can be used to solve surveillance tasks. In this
paper, we propose a novel real-time hyperspectral likelihood maps-aided
tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving
object tracking system generally consists of registration, object detection,
and tracking modules. We focus on the target detection part and remove the
necessity to build any offline classifiers and tune a large amount of
hyperparameters, instead learning a generative target model in an online manner
for hyperspectral channels ranging from visible to infrared wavelengths. The
key idea is that, our adaptive fusion method can combine likelihood maps from
multiple bands of hyperspectral imagery into one single more distinctive
representation increasing the margin between mean value of foreground and
background pixels in the fused map. Experimental results show that the HLT not
only outperforms all established fusion methods but is on par with the current
state-of-the-art hyperspectral target tracking frameworks.

本文提出了一种新的实时高光谱似然图辅助跟踪方法（HLT），通过自适应融合方法将多波段高光谱图像的似然图结合为一个更具区分度的表示，实现了基于在线学习生成目标模型的目标检测，结果表明 HLT 在实现高光谱目标跟踪方面表现优异。