Self-Supervised Learning for Transparent Object Depth Completion Using Depth from Non-Transparent Objects

• URL: http://arxiv.org/abs/2512.05006v1
• Date: Thu, 04 Dec 2025 17:17:47 GMT
• Title: Self-Supervised Learning for Transparent Object Depth Completion Using Depth from Non-Transparent Objects
• Authors: Xianghui Fan, Zhaoyu Chen, Mengyang Pan, Anping Deng, Hang Yang,
• Abstract summary: We propose a new self-supervised method for training depth completion networks.<n>Our method simulates the depth deficits of transparent objects within non-transparent regions.<n> Experiments demonstrate that our method achieves performance comparable to supervised approach.
• Score: 10.093838998509796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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