Multi-view stereo (MVS) implicitly encodes photometric and geometric cues into the cost volume for multi-view correspondence matching, transferring insufficient geometric cues essential to depth estimation and reconstruction. This paper proposes GE-MVS, a novel multi-view stereo network with geometric encoding for more accurate and complete depth estimation and point cloud reconstruction. First, the cross-view adaptive cost volume aggregation module is proposed to strengthen multi-view geometric cues encoding during cost volume construction. Then, the depth consistency optimization is performed in the 3D point space during learning by invoking ground-truth depth cues from adjacent views. Finally, the surface normal geometries are explicitly encoded to refine the sampled depth hypotheses to be consistent in the local neighbor regions. Extensive experiments on the standard MVS benchmarks including DTU, Tanks and Temples, and BlendedMVS demonstrate the state-of-the-art depth estimation and point cloud reconstruction performance of GE-MVS. The GE-MVS is further deployed in real-world experiments for UAV-based large-scale reconstruction, where our method outperforms the prevalent industrial reconstruction solutions concerning reconstruction efficiency and efficacy. Our project page is: https://cuhk-usr-group.github.io/GE-MVS/

Recent advancements in learning-based multi-view stereo (MVS) have demonstrated significant improvements over traditional counterpart, primarily due to the extensive availability of multi-view training images with ground-truth metric depths in the terrestrial in-air domain. However, underwater multi-view stereo (UwMVS) faces substantial challenges arising from the domain gap between in-air and underwater environments, leading to degraded performance when applying in-air MVS models to underwater scenarios. Furthermore, the progress of learning-based UwMVS methods has been hindered by the scarcity of underwater multi-view images with ground-truth depth maps and point clouds. In this paper, we address these challenges by introducing a physically-guided approach for synthesizing underwater multi-view images and present the first large-scale UwMVS dataset for end-to-end training and evaluation of learning-based UwMVS methods. Furthermore, we propose a novel UwMVS network that enhances geometric cue encoding to achieve more accurate and complete point cloud reconstruction. Extensive experiments on our dataset and real-world underwater scenes demonstrate that our dataset enables the trained models for underwater dense reconstruction and that our method achieves state-of-the-art performance in underwater reconstruction. Dataset, code and appendix are available at: https://cuhk-usr-group.github.io/UwMVS/

Rapid urbanization and the rising demand for sustainable mobility have accelerated the adoption of dockless bike-sharing systems. However, these systems frequently encounter challenges such as oversupply, inefficient resource allocation, and limited responsiveness to localized demand. To address these issues, this paper proposes the novel AIoT-based and user behavior-driven management system (AUMS)—a comprehensive AIoT-based framework that integrates demand prediction, dynamic clustering, and rebalancing algorithms to optimize the management of dockless bike-sharing services. Unlike existing approaches that focus narrowly on demand prediction, AUMS combines real-time IoT sensor data with user behavior analytics to support continuous, data-driven decision-making. The system employs a closed-loop control structure, enabling the dynamic reconfiguration of bike distribution in response to shifting urban mobility patterns. Its architecture includes three core modules: 1) demand prediction based on spatiotemporal behavioral data, 2) dynamic clustering to localize operational zones, and 3) intelligent rebalancing to minimize idle rates and unmet demand. The significance of this work lies in its holistic design and rigorous real-world validation. Over a 15-month period, AUMS was deployed in three escalating phases: a localized pilot, a district-level deployment in Tseung Kwan O, and a city-wide experiment across 13 districts in Hong Kong. Field results demonstrate notable performance improvements, including a 10.8% increase in bike utilization, 11.6% growth in trip frequency, and a 29.2% reduction in unmet demand.

Promoting smart city applications can facilitate sustainable development to achieve carbon neutrality and solve existing problems, such as the frequent occurrence of extreme weather events caused by climate change. However, monitoring a large area in detail is very challenging, and there is currently no cost-effective solution. This research aims to design an artificial intelligence (AI) of Things (AIoT) LoRaWAN-based low-power, extensive coverage monitoring and alarm system at a low cost. Using the LoRaWAN communication system, it can provide point-to-point communication distances of more than 1 km, forming a low-cost remote network. Additionally, low-power consumption cameras and temperature and humidity sensors monitor the environment for a long time, then use the image compression and data transmission methods developed in this research to achieve stable and low data rate transmission of images and data. On the other hand, AI image analysis algorithms are used for image monitoring and object detection to provide alarm functions. Through this research, this system successfully monitored the environmental data and images under different extreme weather conditions in Hong Kong and provided effective warnings. Based on this research, a low-cost remote monitoring network can be further formed to automatically and effectively provide environmental monitoring and alerts to local governments over a long period.

LoRa is a promising choice for deploying an IoT network due to its lightweight feature and the extensive support by LoRa Alliance. However, as a fundamental part of LoRa, the typical LoRaWAN protocol confronts severe security challenges because it insecurely utilizes AES-128 to support the low-cost feature. In this paper, we propose a systematic solution that is compatible with LoRaWAN for IoT applications. We extend the standard LoRaWAN protocol with public-key infrastructures. Public-key features like Key exchange and authentication are supported by lightweight hardware implementations of SHA-2, ECDH, EdDSA, and TRNG. A lightweight RISC-V processor with a security coprocessor is implemented and verified using FPGA technology. The security protocol and the prototype hardware system are validated and evaluated on practical applications from our industrial partner. The prototyped development board consumes a static power of 0.116Wand a dynamic power of 0.206 W. The proposed system can achieve a 5.6x-144.7x speed up and reduce memory usage by 2.4x-12.3x for security computations.

A novel computer input system-the Micro Input Devices System (MIDS)-is under development by merging MEMS sensors and existing wireless technologies. This system could potentially replace the functions of the mouse, pen, and keyboard as input devices to the computer. The system could also be used as a general wireless 3D motion sensing device. In this paper, we will present our work on using MIDS for motion sensing application of robot hands. MIDS is used to evaluate the performance of PD adaptive control and Impedance control schemes in manipulating a five-fingered robot hand and in manipulating this hand to grasp a ball. Experimental results indicate that MIDS is capable of obtaining real-time 3D acceleration/vibration data wirelessly for the robotic hand, hence eliminating the need to perform the time-consuming integration of the position sensor data to obtain acceleration. Moreover, our initial results also indicate that further exploration of this technology could eventually produce a new control-input device for robotic grasping manipulators. These results are presented in this paper.

The evolution of human-to-computer input devices lags far behind the evolution of processing power. In this paper, we present work on merging MEMS force sensors and existing wireless technologies to develop a novel multifunctional interface input system, the Micro Input Devices System (MIDS), which could potentially replace the mouse, the pen, and the keyboard as input devices to the computer. Moreover, initial experimental results indicate that further exploration of this technology could eventually produce a new control-input device for grasping robotic manipulators. We have thus far developed a prototype MIDS that consists of two MIDS rings, each packaged with commercial MEMS acceleration sensors to sense multi-axes motion, and a MIDS wrist watch that communicates with the rings and transmits data wirelessly to interface with a CPU. The system has been demonstrated to perform click and drawing motions successfully. A self-calibration method was also developed to resolve ambiguities in sensed motion for the MEMS sensors.