28 accepted papers
Event camera offers advantages in object detection tasks for its properties such as high-speed response, low latency, and robustness to motion blur. However, event cameras inherently lack texture and color information, making open-vocabulary detection particularly challenging. Current event-based de…
We propose an automated genomic interpretation module that transforms raw DNA sequences into actionable, interpretable decisions suitable for integration into medical automation and robotic systems. Our framework combines Chaos Game Representation (CGR) with a Concept Bottleneck Model (CBM), enforci…
This paper presents an innovative approach for non-destructive 3D modeling of plant root structures, which are essential for nutrient and water uptake. While Ground Penetrating Radar (GPR) has been used for detecting subsurface objects with well-defined shapes, such as pipes, accurately reconstructi
Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it
Gas concentration estimation is crucial for understanding and mitigating climate change. While most research and monitoring efforts focus on major greenhouse gases such as CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, significantly less att
Depth estimation is a fundamental task in 3D geometry. While stereo depth estimation can be achieved through triangulation methods, it is not as straightforward for monocular methods, which require the integration of global and local information. The Depth from Defocus (DFD) method utilizes camera l
Fisheye cameras, with their ultra-wide field of view, offer significant benefits for depth estimation in applications such as autonomous navigation, robotics, and immersive imaging by capturing more scene content from a single viewpoint. However, their strong radial distortion and varying spatial re
Keypoint detection and local feature description are fundamental tasks in robotic perception, critical for applications such as SLAM, robot localization, feature matching, pose estimation, and 3D mapping. While existing methods predominantly operate on RGB images, we propose a novel network that dir
Deep neural networks (DNNs) have gained significant attention in 3D object reconstruction. However, detecting and reconstructing hidden or buried objects underground remains a challenging task. Ground Penetrating Radar (GPR) has emerged as a cost-effective and non-destructive technology for subsurfa
Scene reconstruction has a wide range of applications in computer vision and robotics. To build practical constraints and feature Scene reconstruction has a wide range of applications in computer vision and robotics. To build practical constraints and feature correspondences, rich textures and disti…
Depth estimation is a core problem in robotic perception and vision tasks, but 3D reconstruction from a single image presents inherent uncertainties. Current depth estimation models primarily rely on inter-image relationships for supervised training, often overlooking the intrinsic information provi…
Depth estimationn is a critical topic for robotics and vision-related tasks. In monocular depth estimation, in comparison with supervised learning that requires expensive ground truth labeling, self-supervised methods possess great potential due to no labeling cost. However, self-supervised learning…
Reconstructing accurate object shapes based on single image inputs is still a critical and challenging task, mainly due to the potential shape ambiguity and occlusion. Most existing single image 3D reconstruction approaches, either trained on stereo setting or structure-from-motion, estimate 2.5D vi…
The SLAM system plays a pivotal role in robotic mapping and localization, leveraging various sensor technologies to achieve precision. Traditional passive sensors, such as RGB cameras, offer high-resolution imagery at a lower cost for SLAM applications, yet they fall short in accurately estimating 3…
Thermal imaging is effective in low-light or night-time conditions due to its ability to capture thermal radiation differences, but lacks texture compared to visible images. Conversely, visible images retain more texture information, particularly during the daytime, but perform poorly at night. To a…
Large-scale in-situ 3D reconstruction of crop fields presents a challenging task, as the 3D crop structures play a crucial role in plant phenotyping and significantly influence crop growth and yield. While existing efforts focus on close-range plants, only a limited number of deep learning-based met…
Unsupervised visual odometry as an active topic has attracted extensive attention, benefiting from its label-free practical value and robustness in real-world scenarios. However, the performance of camera pose estimation and tracking through deep neural network is still not as ideal as most other ta…
Bayer pattern is a widely used Color Filter Array (CFA) for digital image sensors, efficiently capturing different light wavelengths on different pixels without the need for a costly ISP pipeline. The resulting single-channel raw Bayer images offer benefits such as spectral wavelength sensitivity an…
Single image 3D face reconstruction with accurate geometric details is a critical and challenging task due to the similar appearance on the face surface and fine details in organs. In this work, we introduce a self-supervised 3D face reconstruction approach from a single image that can recover detai…
Camera calibration is a necessary prerequisite in many applications of robotics, especially in robot vision in order to obtain metric reconstruction from a 2D image. In this paper, we address the problem of calibrating from a single image of a surface of revolution (SOR) based on deep learning, in o…
Feature detection and extraction is considered to be one of the most important aspects when it comes to any computer vision application, especially the autonomous driving field that is highly dependent on it. Thermal imaging is less explored in the field of autonomous driving mainly due to the high…
Self-supervised depth estimation has recently demonstrated promising performance compared to the supervised methods on challenging indoor scenes. However, the majority of efforts mainly focus on exploiting photometric and geometric consistency via forward image warping and backward image warping, ba
This paper proposes a novel deep neural network-based method for stereo image rectification. The neural network is mainly based on the theoretical basis of epipolar constraints from multi-view geometry and intensity constraints of images, which separately describes the relationship of the correspond…
Infrared Radiation (IR) images that capture the reflected IR signals from the surrounding environment have been applied to pedestrian detection and rescue tasks, especially for thermal images reflecting the surface temperature of the objects at nighttime. However, feature detection, extraction and m
In this paper, we develop a novel visual-inertial navigation system with motion constraint (VINS-Motion), which extends the visual-inertial navigation system (VINS) to incorporate vehicle motion constraints for improving the autonomous vehicles localization accuracy. Besides the prior information, I…
Deep neural networks have significantly enhanced the performance of various computer vision tasks, including single image depth estimation and image segmentation. However, most existing approaches handle them in supervised manners and require a large number of ground truth labels that consume extens…
We propose to represent the shape of an organ using a neural network classifier. The shape is represented by a function learned by a neural network. Radial Basis Function (RBF) is used as the activation function for each perceptron. The learned implicit function is a combination of radial basis func…
Image-based localization is an essential complement to GPS localization. Current image-based localization methods are based on either 2D-to-3D or 3D-to-2D to find the correspondences, which ignore the real scene geometric attributes. The main contribution of our paper is that we use a 3D model recon…