IEEE

MLMF: A Manifold Learning-Based Multi-Representation Fusion Model for 3D Object Detection with 4D Radar Point Cloud

Abstract: Accurate and reliable environment perception is crucial for developing effective autonomous driving systems. While 4D radar provides significant benefits over LiDAR and cameras in terms of ...
IEEE

Efficient AR Object Alignment Using Deep Closest Point and Cylinder-Angle Interpolation

Abstract: Accurate alignment of virtual objects in Augmented Reality (AR) is essential for precision-critical applications such as surgery, infrastructure inspection, and digital twin systems. However ...
IEEE

Dual-Stream Global Relationship Learning for Oriented Object Detection in Remote Sensing Images

Abstract: Oriented object detection has attained remarkable progress in addressing the challenges associated with rotating invariant feature extraction. However, most existing object detection most ...
IEEE

6-D Object Pose Estimation Based on Point Pair Matching for Robotic Grasp Detection

Abstract: The 6-D pose estimation is a critical work essential to achieve reliable robotic grasping. Currently, the prevalent method is reliant on keypoint correspondence. However, this approach ...
IEEE

FIFO-MEP: An Efficient Multi-Eviction-Point FIFO Cache with Stable Demotion for Burst-Oriented Access Mitigation

Abstract: Caching technology is widely used in multiple areas particularly in distributed computing, where its performance is highly dependent on the cache efficiency. The cache eviction algorithm ...
IEEE

End-to-End 3D Object Detection Using LiDAR Point Cloud

Abstract: Significant progress has been made in the field of au-tonomous vehicles. Object detection and tracking are the primary tasks for any autonomous vehicle. The task of object detection in ...
IEEE

3D Point Cloud Object Detection on Edge Devices for Split Computing

Abstract: The field of autonomous driving technology is rapidly advancing, with deep learning being a key component. Particularly in the field of sensing, 3D point cloud data collected by LiDAR is ...
IEEE

WBF-ODAL: Weighted Boxes Fusion for 3D Object Detection from Automotive LiDAR Point Clouds

Abstract: The use of Convolutional Neural Networks (CNNs) is the state-of-the-art for 3D object detection from automotive/vehicle LiDAR point clouds. However, not all models perform uniformly well ...
IEEE

FPNFormer: Rethink the Method of Processing the Rotation-Invariance and Rotation-Equivariance on Arbitrary-Oriented Object Detection

Abstract: Feature pyramid network transformer decoder (FPNFormer) module, which can effectively deal with the strong rotation arbitrary of remote sensing images while improving the expressiveness and ...
IEEE

Occlusion-Aware Text-Image-Point Cloud Pretraining for Open-World 3D Object Recognition

Abstract: Recent open-world representation learning approaches have leveraged CLIP to enable zero-shot 3D object recognition. However, performance on real point clouds with occlusions still falls ...
IEEE

Point-Supervised Oriented Ship Detection via Segment Anything Model for SAR Images

Abstract: Conventional ship detection methods for synthetic aperture radar (SAR) images typically require complete annotations, which are time-consuming. Hence, we propose a framework named ...
IEEE

Unraveling Vanishing Point And Calibrating Tiny Objects For Semantic Scene Completion

Abstract: Semantic Scene Completion (SSC) aims to jointly predict semantic categories and 3D occupancy of a scene from coarse inputs, which is crucial for providing reliable perception in autonomous ...