无需标注数据，自监督学习实现透明物体深度补全
Self-Supervised Learning for Transparent Object Depth Completion Using Depth from Non-Transparent Objects
- 用非透明区域模拟透明物体深度缺失，以原始深度图为监督信号
- 在无标注数据下性能接近有监督方法，小样本时预训练提升效果
- 适合缺乏标注数据的透明物体深度感知场景
透明物体的感知是计算机视觉中的经典难题。传统深度传感器因光线折射和反射难以准确获取透明物体的深度信息。以往研究通常训练神经网络对传感器获取的深度进行补全，可快速准确地生成透明物体的深度图，但依赖大量标注数据，深度图标注成本高。为此，本文提出一种新的自监督深度补全网络训练方法：在非透明区域模拟透明物体的深度缺失，并利用原始深度图作为监督信号。实验表明，该方法性能可媲美有监督方法；在训练样本较少时，使用本方法进行预训练能显著提升模型表现。
原文摘要 · Abstract (English)
The perception of transparent objects is one of the well-known challenges in computer vision. Conventional depth sensors have difficulty in sensing the depth of transparent objects due to refraction and reflection of light. Previous research has typically train a neural network to complete the depth acquired by the sensor, and this method can quickly and accurately acquire accurate depth maps of transparent objects. However, previous training relies on a large amount of annotation data for supervision, and the labeling of depth maps is costly. To tackle this challenge, we propose a new self-supervised method for training depth completion networks. Our method simulates the depth deficits of transparent objects within non-transparent regions and utilizes the original depth map as ground truth for supervision. Experiments demonstrate that our method achieves performance comparable to supervised approach, and pre-training with our method can improve the model performance when the training samples are small.
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