Robust Symmetry Prediction with Multi-Modal Feature Fusion for Partial Shapes

In geometry processing, symmetry research benefits from global geo-metric features of complete shapes, but the shape of an object captured in real -world applications is often incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Different from the existing works predicting symmetry from the complete shape, we propose a learning approach for symmetry prediction based on a single RGB-D image. Instead of directly predicting the symmetry from incomplete shapes, our method consists of two modules, i.e., the multi -mod-al feature fusion module and the detection-by-reconstruction module. Firstly, we build a channel-transformer network (CTN) to extract cross-fusion features from the RGB-D as the multi-modal feature fusion module, which helps us aggregate features from the color and the depth separately. Then, our self-reconstruction net-work based on a 3D variational auto-encoder (3D-VAE) takes the global geo-metric features as input, followed by a prediction symmetry network to detect the symmetry. Our experiments are conducted on three public datasets: ShapeNet, YCB, and ScanNet, we demonstrate that our method can produce reliable and accurate results.