16 accepted papers
This paper establishes an analytical model for a dual-pressure-actuated pneumatic linear actuator, investigating the relationship between the output force of the linear actuator and both the pressure differential and displacement. Experiments were designed to validate the model. The maximum output f
Knowledge distillation (KD) is a model compression technique that transfers knowledge from a large teacher model to a smaller student model to enhance its performance. Existing methods often assume that the student model is inherently inferior to the teacher model. However, we identify that the fund…
Point cloud upsampling is a critical challenge in 3D vision, particularly for large-scale, real-world data. We propose ASFNet, a novel implicit neural network-based approach that uniquely combines adaptive spatial feature representation with efficient spatial hashing. This method significantly impro…
Autonomous ships utilize automation systems to achieve unmanned navigation, driving innovation in maritime transportation. However, sea conditions, influenced by dynamic factors such as wave height, wind speed, and ocean currents, present a challenge in accurately assessing these conditions. Traditi
Accurate polyp sizing during endoscopy is crucial for cancer risk assessment but is hindered by subjective methods and inadequate datasets lacking integrated 2D appearance, 3D structure, and real-world size information. We introduce PolypSense3D, the first multi-source benchmark dataset specifically…
Getting dense, uniform, time-series point cloud data is critical for effective rendering. However, due to the limited computational power of edge devices, existing methods cannot achieve real-time results, which affects the visual quality of the consumer experience. To effectively address this issue…
Manipulating transparent objects presents significant challenges due to the complexities introduced by their reflection and refraction properties, which considerably hinder the accurate estimation of their 3D shapes. To address these challenges, we propose a single-view RGB-D-based depth completion
Human intention understanding in untrimmed videos aims to watch a natural video and predict what the person’s intention is. Currently, exploration of predicting human intentions in untrimmed videos is far from enough. On the one hand, untrimmed videos with mixed actions and backgrounds have a signif…
The emergence of non-fingered soft bioinspired grippers poses a challenge for learning-based grasping control due to the lack of a model describing grasping robustness and a dataset for training. In this letter, we propose a comprehensive pipeline encompassing grasping evaluation, dataset generation
Soft wearable devices for hand rehabilitation can assist activities of daily living. While existing soft robotic gloves can help with finger flexion, they are less suitable for patients who cannot extend their fingers due to poststroke increased muscular tension. In this letter, we proposed a variab
In a long-term large-scenario application, the multi-agent collaborative SLAM is expected to improve the robustness and efficiency of executing tasks for mobile agents. In this paper, a multi-agent collaborative visual-inertial SLAM system is proposed based on a centralized client-server (CS) archit…
The efficiency and accuracy of mapping are crucial in a large scene and long-term AR applications. Multi-agent cooperative SLAM is the precondition of multi-user AR interaction. The cooperation of multiple smart phones has the potential to improve efficiency and robustness of task completion and can…
Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU). Apart from the latter task is much more challenging, the main cause is that recent approaches learn human interactive relations via shallow graphical models…
In this paper, we present Calibration Recurrent Convolutional Neural Network (CalibRCNN) to infer a 6 degrees of freedom (DOF) rigid body transformation between 3D LiDAR and 2D camera. Different from the existing methods, our 3D-2D CalibRCNN not only uses the LSTM network to extract the temporal fea…
Treating graph structures of Markov random fields as unknown and estimating them jointly with labels have been shown to be useful for modeling human activity recognition and other related tasks. We propose two novel relaxations for solving this problem. The first is a linear programming (LP) relaxat…
In recent years, many excellent works on visual-inertial SLAM and laser-based SLAM have been proposed. Although inertial measurement unit (IMU) significantly improve the motion estimate performance by reducing the impact of illumination variation or texture-less region on visual tracking, tracking f…