Abstract
The local reference frame (LRF) acts as a critical role in 3D local shape description and matching. However, most existing LRFs are hand-crafted and suffer from limited repeatability and robustness. This paper presents the first attempt to learn an LRF via a Siamese network that needs weak supervision only. In particular, we argue that each neighboring point in the local surface gives a unique contribution to LRF construction and measure such contributions via learned weights. Extensive analysis and comparative experiments on three public datasets addressing different application scenarios have demonstrated that LRF-Net is more repeatable and robust than several state-of-the-art LRF methods (LRF-Net is only trained on one dataset). We show that LRFNet achieves 0.686 MeanCos performance on the UWA 3D modeling (UWA3M) dataset, outperforming the closest method by 0.18. In addition, LRF-Net can significantly boost the local shape description and 6-DoF pose estimation performance when matching 3D point clouds.
Highlights
The local reference frame (LRF) is a canonical coordinate system established in the 3D local surface, which is a useful geometric cue for 3D point clouds
Two corresponding local surfaces can be converted into the same pose and full 3D geometric information can be employed, which is beneficial to improving the performance of local descriptors
We propose a learned approach toward LRF estimation, which considers the contribution of all neighboring points (Figure 1)
Summary
The local reference frame (LRF) is a canonical coordinate system established in the 3D local surface, which is a useful geometric cue for 3D point clouds. Most CA-based LRFs still suffer from sign ambiguity, and PSD-based LRFs show limited robustness to high levels of noise and variations of mesh resolution [10] Methods in both classes usually apply a weighted strategy to improve their repeatability performance. Unlike previous CA-based and PSD-based approaches, such learned strategy of determining weights is shown to be invariant to rigid transformation and robust to noise, clutter, occlusion and varying mesh resolutions. LRF-Net can significantly boost the local shape description and 6-DoF pose estimation performance when matching 3D point clouds. LRF-Net, based on a Siamese network that needs weak supervision only, is proposed that achieves the state-of-the-art repeatability performance under the impacts of noise, varying mesh resolutions, clutter and occlusion. LRF-Net can significantly boost the performance of local shape description and 6-DoF pose estimation.
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