Semantic Segmentation of LiDAR Points Clouds: Rasterization Beyond Digital Elevation Models
نویسندگان
چکیده
منابع مشابه
Accuracy Assessment of Lidar-derived Digital Elevation Models
Despite the relatively high cost of airborne lidar-derived digital elevation models (DEMs), such products are usually presented without a satisfactory associated estimate of accuracy. For the most part, DEM accuracy estimates are typically provided by comparing lidar heights against a finite sample of check point coordinates from an independent source of higher accuracy, supposing a normal dist...
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The fusion of digital surfaces, their optimal combination into a new single dataset, is a crucial topic in the geomatic sciences. Nowadays, sensors and processing techniques provide for the same site Digital Elevation Models (DEMs) with different geometric characteristics and accuracy. Each DEM contains intrinsic errors due to the primary data acquisition technology and processing methodology s...
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The paper presents the results of an accuracy analysis of a number of gridded Digital Surface Models (DSMs) which have been created from first return laser scanning (LiDAR) data. DSMs are created using a variety of methods, and differences between the models and the raw LiDAR data are quantified and the spatial patterns of the errors explored. The results presented in this paper demonstrate the...
متن کاملGenerate Digital Elevation Models Using Laser Altimetry (LIDAR) Data
A Laser Altimetry (LIDAR) system aboard an aircraft can yield highly accurate data about the ground surface and vegetation below. Raw LIDAR points must be processed to generate a digital elevation model (DEM), i.e. a digital map of the terrain surface. A number of techniques can be used to generate the DEM, and in this report I investigate several of these including a new algorithm based on rec...
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3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks (NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level...
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ژورنال
عنوان ژورنال: IEEE Geoscience and Remote Sensing Letters
سال: 2020
ISSN: 1545-598X,1558-0571
DOI: 10.1109/lgrs.2019.2958858