The automatic recognition of 3D objects with "uncooperative" surfaces is a difficult and slow process. The Fraunhofer IOF has ...

This README file documents the results for both Mid-Term Project:3D Object Detection and Final Project ... Step 1: Computing Lidar Point-Cloud from Range Image In the Waymo Open dataset, lidar data is ...

While the fusion of 2D images and 3D LiDAR data leads to more accurate 3D detection results, it still faces its own set of challenges, with accurate detection of small objects remaining difficult.

For example a sample may contain a full lidar sweep, front, back and side camera images, and a full radar sweep. Our implementation relies only on the lidar data. A sample also contains a set of 3D ...

Abstract: In this paper, a lightweight 3D object detection model using color and depth images is proposed. In recent years, several studies have focused on the application of deep learning to object ...

Abstract: Existing monocular 3D object detection methods have been demonstrated on rectilinear perspective images and fail in images with alternative projections such as those acquired by fisheye ...

To tackle this issue, scientists have developed multi-modal 3D object detection methods that combine 3D LiDAR data with 2D RGB images taken by standard cameras. While the fusion of 2D images and 3D ...

Similarly, one can think of a convolution network for an image ... 3D object detection as well as 3D semantic segmentation (classify each point in space as belonging to an object category). An ...

For example, if you want to detect a book cover in AR, you can extract features from the cover image ... for AR object detection is model-based detection. This technique involves using 3D models ...