Abstract: LiDAR, as an excellent sensor, can provide positions, motion states, and other objective attribute information of objects in the 3D world. Inevitably, the inherent sparsity of point cloud ...
We introduce TASTE-Rob: 1) a dataset with 100,856 task-oriented hand-object interaction videos, 2) a three-stage pose-refinement video generation pipeline. With the above contributions, TASTE-Rob is ...
GenAI may be accelerating a developmental transition in how learners conceptualize programming itself.
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 ...
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 ...
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 ...
Oriented object detection has gained increasing attention due to its ability to detect objects with arbitrary orientations in the field of remote sensing (RS) images. However, the laborious task of ...
Abstract: With significant potential in autonomous driving, and robotics, 3D object detection has garnered increasing attention among researchers. Leveraging its reliability in depth information, ...
Abstract: Learning object affordances is an effective tool in the field of robot learning. While the data-driven models investigate affordances of single or paired objects, there is a gap in the ...
Abstract: Fast and accurate three-dimensional (3D) Multiple Object Detection and Tracking (3DMODT) is a critical task for autonomous vehicles to perceive their surroundings and make safe decisions.
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