Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding
We present an improved approach for 3D object detection in point cloud data based on the Frustum PointNet (F-PointNet). Compared to the original F-PointNet, our newly proposed method considers the point neighborhood when computing point features. The newly introduced local neighborhood embedding operation mimics the convolutional operations in 2D neural networks. Thus features of each point are not only computed with the features of its own or of the whole point cloud but also computed especially with respect to the features of its neighbors. Experiments show that our proposed method achieves better performance than the F-Pointnet baseline on 3D object detection tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Object DetectionObjectobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
Surface-biased Multi-Level Context 3D Object Detection
Object detection in 3D point clouds is a crucial task in a range of computer vision applications including robotics, autonomous cars, and augmented reality. This work addresses the object detection task in 3D point cloud…
3D Object DetectionObjectobject-detectionObject DetectionTowards Consistent Object Detection via LiDAR-Camera Synergy
As human-machine interaction continues to evolve, the capacity for environmental perception is becoming increasingly crucial. Integrating the two most common types of sensory data, images, and point clouds, can enhance d…
Objectobject-detectionObject DetectionPosition3D Siamese Transformer Network for Single Object Tracking on Point Clouds
Siamese network based trackers formulate 3D single object tracking as cross-correlation learning between point features of a template and a search area. Due to the large appearance variation between the template and sear…
3D Single Object TrackingObject TrackingSimulation-to-Reality domain adaptation for offline 3D object annotation on pointclouds with correlation alignment
Annotating objects with 3D bounding boxes in LiDAR pointclouds is a costly human driven process in an autonomous driving perception system. In this paper, we present a method to semi-automatically annotate real-world poi…
Autonomous DrivingDomain AdaptationObjectobject-detection+1Frustum PointNets for 3D Object Detection from RGB-D Data
In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns and invariances of 3D data, we direct…
3D Object DetectionObjectObject DetectionObject Detection In Indoor Scenes+2