arXiv subjects

Binghua Su

Publications and source records attributed to Binghua Su.

3 recordsLinked to original sources

arXiv2025

Endoscopic Depth Estimation Based on Deep Learning: A Survey

Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted considerable attention from researchers in medical imaging, computer vision, and robotics. Over the past decade, a large number of methods have been developed. Despite the existence of several related surveys, a comprehensive overview focusing on recent deep learning-based techniques is still limited. This paper endeavors to bridge this gap by comprehensively reviewing the state-of-the-art literature. Specifically, we provide a thorough survey of the field from three key perspectives: data, methods, and applications. Firstly, at the data level, we describe the acquisition process of publicly available datasets. Secondly, at the methodological level, we introduce both monocular and stereo deep learning-based approaches for endoscopic depth estimation. Thirdly, at the application level, we identify the specific challenges and corresponding solutions for the clinical implementation of depth estimation technology, situated within concrete clinical scenarios. Finally, we outline potential directions for future research, such as domain adaptation, real-time implementation, and the synergistic fusion of depth information with sensor technologies, thereby providing a valuable starting point for researchers to engage with and advance the field toward clinical translation.

cs.CV↗

arXiv2025

HDBFormer: Efficient RGB-D Semantic Segmentation with A Heterogeneous Dual-Branch Framework

In RGB-D semantic segmentation for indoor scenes, a key challenge is effectively integrating the rich color information from RGB images with the spatial distance information from depth images. However, most existing methods overlook the inherent differences in how RGB and depth images express information. Properly distinguishing the processing of RGB and depth images is essential to fully exploiting their unique and significant characteristics. To address this, we propose a novel heterogeneous dual-branch framework called HDBFormer, specifically designed to handle these modality differences. For RGB images, which contain rich detail, we employ both a basic and detail encoder to extract local and global features. For the simpler depth images, we propose LDFormer, a lightweight hierarchical encoder that efficiently extracts depth features with fewer parameters. Additionally, we introduce the Modality Information Interaction Module (MIIM), which combines transformers with large kernel convolutions to interact global and local information across modalities efficiently. Extensive experiments show that HDBFormer achieves state-of-the-art performance on the NYUDepthv2 and SUN-RGBD datasets. The code is available at: https://github.com/Weishuobin/HDBFormer.

cs.CV↗

arXiv2022

GIU-GANs: Global Information Utilization for Generative Adversarial Networks

In recent years, with the rapid development of artificial intelligence, image generation based on deep learning has dramatically advanced. Image generation based on Generative Adversarial Networks (GANs) is a promising study. However, since convolutions are limited by spatial-agnostic and channel-specific, features extracted by traditional GANs based on convolution are constrained. Therefore, GANs are unable to capture any more details per image. On the other hand, straightforwardly stacking of convolutions causes too many parameters and layers in GANs, which will lead to a high risk of overfitting. To overcome the aforementioned limitations, in this paper, we propose a new GANs called Involution Generative Adversarial Networks (GIU-GANs). GIU-GANs leverages a brand new module called the Global Information Utilization (GIU) module, which integrates Squeeze-and-Excitation Networks (SENet) and involution to focus on global information by channel attention mechanism, leading to a higher quality of generated images. Meanwhile, Batch Normalization(BN) inevitably ignores the representation differences among noise sampled by the generator, and thus degrade the generated image quality. Thus we introduce Representative Batch Normalization(RBN) to the GANs architecture for this issue. The CIFAR-10 and CelebA datasets are employed to demonstrate the effectiveness of our proposed model. A large number of experiments prove that our model achieves state-of-the-art competitive performance.

cs.LG↗