从激光雷达和航空图像中提取物体高度

Jesus Guerrero
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引用次数: 0

摘要

本作品展示了一种从激光雷达和航空图像中提取物体高度的程序方法。我们讨论了如何获取高度以及激光雷达和图像处理的未来。SOTA 物体分割使我们能够在没有深度学习背景的情况下获取物体高度。工程师们将对世界数据进行跨代跟踪和重新处理。他们将使用像本文这样的旧程序方法和本文讨论的新方法。SOTA 方法正在超越分析,进入生成式人工智能领域。我们既包括程序方法,也包括使用语言模型的新方法。这些方法包括点云、图像和文本编码,可实现空间感知人工智能。
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Extracting Object Heights From LiDAR & Aerial Imagery
This work shows a procedural method for extracting object heights from LiDAR and aerial imagery. We discuss how to get heights and the future of LiDAR and imagery processing. SOTA object segmentation allows us to take get object heights with no deep learning background. Engineers will be keeping track of world data across generations and reprocessing them. They will be using older procedural methods like this paper and newer ones discussed here. SOTA methods are going beyond analysis and into generative AI. We cover both a procedural methodology and the newer ones performed with language models. These include point cloud, imagery and text encoding allowing for spatially aware AI.
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