Towards Robust Object Detection in Unseen Catheterization Laboratories

Deep-learning-based object detectors, while offering exceptional performance, are data-dependent and can suffer from generalization issues. In this thesis, we investigated deep neural networks for detecting people and medical instruments in the vision-based workflow analysis system inside Catheterization Laboratories (Cath Labs). The central...

PointeNet: A lightweight framework for effective and efficient point cloud analysis

The conventional wisdom in point cloud analysis predominantly explores 3D geometries. It is often achieved through the introduction of intricate learnable geometric extractors in the encoder or by deepening networks with repeated blocks. However, these methods contain a significant number of learnable parameters, resulting in substantial...

Detailed Facade Reconstruction for Mahattan-world Buildings

3D building models play an important role in many real-world applications. Different models are suitable for different application scenarios based on their levels of detail. LOD3 models with facade details are crucial for many applications, such as virtual reality and urban simulation. Currently, 3D building models with lower LOD are largely...

Binary Neural Networks for Object Detection

In the past few years, convolutional neural networks (CNNs) have been widely utilized and shown state-of-the-art performances on computer vision tasks. However, CNN based approaches usually require a large amount of storage, run-time memory, as well as computation power in both training and inference time, which are usually used on GPU based...