10 accepted papers
Mobile robot motion planning heavily relies on grid-based occupancy maps, while existing works require high memory usage and expensive updating overhead. In this work, we propose a memory-lite grid-block data structure and an efficient map updating algorithm for LiDAR-based online exploration-orient…
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…
The recent 3D Gaussian Splatting simultaneous localization and mapping (3DGS-SLAM) has achieved high-fidelity reconstruction from RGB-D images. However, the input lacks edge information in the scene, resulting in inability of Gaussians to accurately model edges and appearance of artifacts at object
Real-time high-accuracy optical flow estimation is critical for a variety of real-world robotic applications. However, current learning-based methods often struggle to balance accuracy and computational efficiency: methods that achieve high accuracy typically demand substantial processing power, whi
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…
Low-light enhancement and deblurring is vital for high-level vision-related nighttime tasks. Most existing cascade and joint enhancement methods may provide undesirable results, suffering from severe artifacts, deteriorating blur, and unclear details. In this paper, we propose a novel ambiguity-awar…
Developing an efficient collision avoidance policy with Spiking Reinforcement Learning for dynamic, decision-making agents remains challenging. Moreover, the implementation of energy-efficient collision avoidance is important for mobile robots that operate with limited on-board computing resources.…
Multimodal image pairs can make object detection more reliable in challenging environments, so RGB-T object detection has gained extensive attention over the past decade. To alleviate the complementarity of the visible and thermal modality, we propose a novel lightweight Feature Enhancement-fusion M…
Real-time high-accuracy optical flow estimation is a crucial component in various applications, including localization and mapping in robotics, object tracking, and activity recognition in computer vision. While recent learning-based optical flow methods have achieved high accuracy, they often come…
Recurrent neural networks (RNNs), particularly long short-term memory (LSTM), have gained much attention in automatic speech recognition (ASR). Although some successful stories have been reported, training RNNs remains highly challenging, especially with limited training data. Recent research found…