DCDet: Dynamic Cross-based 3D Object Detector
Recently, significant progress has been made in the research of 3D object detection. However, most prior studies have focused on the utilization of center-based or anchor-based label assignment schemes. Alternative label assignment strategies remain unexplored in 3D object detection. We find that the center-based label assignment often fails to generate sufficient positive samples for training, while the anchor-based label assignment tends to encounter an imbalanced issue when handling objects of varying scales. To solve these issues, we introduce a dynamic cross label assignment (DCLA) scheme, which dynamically assigns positive samples for each object from a cross-shaped region, thus providing sufficient and balanced positive samples for training. Furthermore, to address the challenge of accurately regressing objects with varying scales, we put forth a rotation-weighted Intersection over Union (RWIoU) metric to replace the widely used L1 metric in regression loss. Extensive experiments demonstrate the generality and effectiveness of our DCLA and RWIoU-based regression loss. The Code will be available at https://github.com/Say2L/DCDet.git.
Code (1)
Tasks
3D Object DetectionObjectobject-detectionObject DetectionregressionSimilar Papers 제목 키워드 기반
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn…
Anomaly DetectionContrastive LearningRepresentation LearningTime Series+1DCDetector: An IoT terminal vulnerability mining system based on distributed deep ensemble learning under source code representation
Context: The IoT system infrastructure platform facility vulnerability attack has become the main battlefield of network security attacks. Most of the traditional vulnerability mining methods rely on vulnerability detect…
Ensemble LearningSentenceVulnerability DetectionMAAT: Mamba Adaptive Anomaly Transformer with association discrepancy for time series
Anomaly detection in time series is essential for industrial monitoring and environmental sensing, yet distinguishing anomalies from complex patterns remains challenging. Existing methods like the Anomaly Transformer and…
Anomaly DetectionAnomaly LocalizationMambaTime Series+1Object as Query: Lifting any 2D Object Detector to 3D Detection
3D object detection from multi-view images has drawn much attention over the past few years. Existing methods mainly establish 3D representations from multi-view images and adopt a dense detection head for object detecti…
3D Object DetectionObjectobject-detectionObject DetectionDyRA: Portable Dynamic Resolution Adjustment Network for Existing Detectors
Achieving constant accuracy in object detection is challenging due to the inherent variability of object sizes. One effective approach to this problem involves optimizing input resolution, referred to as a multi-resoluti…
Objectobject-detectionObject DetectionRobust Object Detection