从单张RGB图预测深度图并修复缺失信息
Predicting Depth Maps from Single RGB Images and Addressing Missing Information in Depth Estimation
- 用多层训练法从单张图片生成深度图
- 在Cityscapes数据集上成功修复深度图缺失点
- 适合自动驾驶中深度感知的实用方案
深度成像在自动驾驶系统中至关重要，用于检测和测量车辆周围物体。然而，深度图像中常因像素数据不完整或间隙导致部分点无法测量，形成缺失信息。本研究针对这一问题提出两项关键任务：首先，设计一种多层训练算法，仅基于单张RGB图像生成深度图；其次，利用该算法修复深度图中的缺失区域，实现完整且准确的数据输出。我们在Cityscapes数据集上进行了测试，成功恢复了其深度图中的缺失信息，验证了该方法在真实城市环境中的有效性。
原文摘要 · Abstract (English)
Depth imaging is a crucial area in Autonomous Driving Systems (ADS), as it plays a key role in detecting and measuring objects in the vehicle's surroundings. However, a significant challenge in this domain arises from missing information in Depth images, where certain points are not measurable due to gaps or inconsistencies in pixel data. Our research addresses two key tasks to overcome this challenge. First, we developed an algorithm using a multi-layered training approach to generate Depth images from a single RGB image. Second, we addressed the issue of missing information in Depth images by applying our algorithm to rectify these gaps, resulting in Depth images with complete and accurate data. We further tested our algorithm on the Cityscapes dataset and successfully resolved the missing information in its Depth images, demonstrating the effectiveness of our approach in real-world urban environments.
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