CSPC-Dataset: New LiDAR Point Cloud Dataset and Benchmark for Large-Scale Scene Semantic Segmentation
نویسندگان
چکیده
منابع مشابه
A Large-scale Dataset and Benchmark for Similar Trademark Retrieval
Trademark retrieval (TR) has become an important yet challenging problem due to an ever increasing trend in trademark applications and infringement incidents. There have been many promising attempts for the TR problem, which, however, fell impracticable since they were evaluated with limited and mostly trivial datasets. In this paper, we provide a large-scale dataset with benchmark queries with...
متن کاملSemantic3D.net: A new Large-scale Point Cloud Classification Benchmark
This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the...
متن کاملDiverse Scene Stitching from a Large-Scale Aerial Video Dataset
Diverse scene stitching is a challenging task in aerial video surveillance. This paper presents a hybrid stitching method based on the observation that aerial videos captured in real surveillance settings are neither totally ordered nor completely unordered. Often, human operators apply continuous monitoring of the drone to revisit the same area of interest. This monitoring mechanism yields to ...
متن کاملLarge-scale Point Cloud Semantic Segmentation with Superpoint Graphs
We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of largescale point clouds of millions of points. We argue that the organization of 3D point clouds can be efficiently captured by a structure called superpoint graph (SPG), derived from a partition of the scanned scene into geometrically homogeneous elements. SPGs offer a compact yet rich represen...
متن کاملHigh-Resolution Multispectral Dataset for Semantic Segmentation
Unmanned aircraft have decreased the cost required to collect remote sensing imagery, which has enabled researchers to collect high-spatial resolution data from multiple sensor modalities more frequently and easily. The increase in data will push the need for semantic segmentation frameworks that are able to classify non-RGB imagery, but this type of algorithmic development requires an increase...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2992612