Depth completion of transparent objects based on convolutional attention mechanism

Qingfeng Liang; Limin Liao; Hao Li

doi:10.1117/12.3011392

8 November 2023 Depth completion of transparent objects based on convolutional attention mechanism

Qingfeng Liang, Limin Liao, Hao Li

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129232D (2023) https://doi.org/10.1117/12.3011392
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

Transparent objects have two unique optical properties of reflection and light transmission. Current depth cameras cannot perceive them effectively, and the obtained depth images contain a lot of holes and noise information. Robotic grasping and 3D reconstruction of transparent objects becomes difficult. Recent methods mainly focus on depth completion for nontransparent objects, while transparent objects are rarely studied. This paper presents a deep learning approach to obtain a complete depth image from raw RGB-D images of transparent objects. Specifically, our algorithm uses an encoder-decoder architecture, the encoder is used to extract the feature information of transparent objects, and the decoder uses deconvolution operations to complete the depth of transparent objects. In the encoder stage, this paper adopts the Joint Convolutional Attention and Transformer block (JCAT). It consists of two parts: the convolutional attention layer and the Vision Transformer, which is beneficial to extract local and global features. In the decoder stage, Convolutional Block Attention Module (CBAM) is added to enhance feature fusion in channel and spatial dimensions. We conduct a large number of experiments on the ClearGrasp dataset. The experimental results show that our method is reliable.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qingfeng Liang, Limin Liao, and Hao Li "Depth completion of transparent objects based on convolutional attention mechanism", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129232D (8 November 2023); https://doi.org/10.1117/12.3011392

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KEYWORDS

Image segmentation

RGB color model

Transformers

Deep learning

Image fusion

Cameras

Feature extraction

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