Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving

by   Jiawei Liu, et al.

Recent years have witnessed huge successes in 3D object detection to recognize common objects for autonomous driving (e.g., vehicles and pedestrians). However, most methods rely heavily on a large amount of well-labeled training data. This limits their capability of detecting rare fine-grained objects (e.g., police cars and ambulances), which is important for special cases, such as emergency rescue, and so on. To achieve simultaneous detection for both common and rare objects, we propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes. Specifically, we analyze in-depth differences between images and point clouds, and then present a practical principle for the few-shot setting in the 3D LiDAR dataset. To solve this task, we propose a simple and effective detection framework, including (1) an incremental fine-tuning method to extend existing 3D detection models to recognize both common and rare objects, and (2) a sample adaptive balance loss to alleviate the issue of long-tailed data distribution in autonomous driving scenarios. On the nuScenes dataset, we conduct sufficient experiments to demonstrate that our approach can successfully detect the rare (novel) classes that contain only a few training data, while also maintaining the detection accuracy of common objects.


page 1

page 5


Traffic Context Aware Data Augmentation for Rare Object Detection in Autonomous Driving

Detection of rare objects (e.g., traffic cones, traffic barrels and traf...

SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving

Object detection (OD) is crucial to autonomous driving. Unknown objects ...

Multimodal Detection of Unknown Objects on Roads for Autonomous Driving

Tremendous progress in deep learning over the last years has led towards...

Towards Long-Tailed 3D Detection

Contemporary autonomous vehicle (AV) benchmarks have advanced techniques...

Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception

Self-driving cars must detect vehicles, pedestrians, and other traffic p...

Synthetic Examples Improve Generalization for Rare Classes

The ability to detect and classify rare occurrences in images has import...

A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations

Training neural networks to perform 3D object detection for autonomous d...

Please sign up or login with your details

Forgot password? Click here to reset