3D object detection classifies the object category and estimates oriented 3D bounding boxes of physical objects from 3D sensor data.
( Image credit: AVOD )
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The performance of 3D object detection models over point clouds highly depends on their capability of modeling local geometric patterns.
In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud.
One promising field, inspired by the success of convolutional neural networks (CNNs) in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve.
This paper aims at constructing a light-weight object detector that inputs a depth and a color image from a stereo camera.
In this paper we propose a novel 3D single-shot object detection method for detecting vehicles in monocular RGB images.
3D object detection has been widely studied due to its potential applicability to many promising areas such as robotics and augmented reality.
In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection and obtain more explainable and convincing uncertainty for the prediction.
Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications.
After aligning the interior points with fused features, the proposed network refines the prediction in a more accurate manner and encodes the whole box in a novel compact method.