Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
This addresses the domain adaptation problem for 3D object detection in autonomous driving, enabling detectors to generalize across datasets without retraining, though it is incremental as it builds on existing unsupervised domain adaptation frameworks.
The paper tackles the problem of 3D object detectors overfitting to specific lidar scan patterns in autonomous driving datasets, which reduces accuracy when tested on different datasets, and proposes SEE-VCN, a viewer-centred surface completion network that achieves a unified representation to outperform previous domain adaptation methods in multiple settings.
Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are trained on one dataset and tested on another. We observe that lidar scan pattern differences form a large component of this reduction in performance. We address this in our approach, SEE-VCN, by designing a novel viewer-centred surface completion network (VCN) to complete the surfaces of objects of interest within an unsupervised domain adaptation framework, SEE. With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns. By adopting a domain-invariant representation, SEE-VCN can be classed as a multi-target domain adaptation approach where no annotations or re-training is required to obtain 3D detections for new scan patterns. Through extensive experiments, we show that our approach outperforms previous domain adaptation methods in multiple domain adaptation settings. Our code and data are available at https://github.com/darrenjkt/SEE-VCN.