SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters

ECCV 2018 Yifan XuTianqi FanMingye XuLong ZengYu Qiao

Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds. Towards this we propose a novel convolutional architecture, termed SpiderCNN, to efficiently extract geometric features from point clouds... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
3D Part Segmentation ShapeNet-Part SpiderCNN Class Average IoU 82.4 # 2
3D Part Segmentation ShapeNet-Part SpiderCNN Instance Average IoU 85.3 # 3