Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection. (arXiv:2209.06407v1 [cs.CV])

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 …

arxiv detection domain adaptation unsupervised