Domain Adaptation for 3D Object Detection with Limited Annotations
Research Paper#3D Object Detection, Domain Adaptation, Autonomous Driving🔬 Research|Analyzed: Jan 3, 2026 06:21•
Published: Dec 31, 2025 15:26
•1 min read
•ArXivAnalysis
This paper addresses the critical problem of domain adaptation in 3D object detection, a crucial aspect for autonomous driving systems. The core contribution lies in its semi-supervised approach that leverages a small, diverse subset of target domain data for annotation, significantly reducing the annotation budget. The use of neuron activation patterns and continual learning techniques to prevent weight drift are also noteworthy. The paper's focus on practical applicability and its demonstration of superior performance compared to existing methods make it a valuable contribution to the field.
Key Takeaways
- •Addresses domain adaptation challenges in 3D object detection for autonomous driving.
- •Proposes a semi-supervised approach requiring a small, diverse subset of target domain data.
- •Employs neuron activation patterns and continual learning to improve performance and prevent weight drift.
- •Demonstrates superior performance compared to existing domain adaptation techniques.
Reference / Citation
View Original"The proposed approach requires very small annotation budget and, when combined with post-training techniques inspired by continual learning prevent weight drift from the original model."