论文标题
分层超季度分解与隐式空间分离
Hierarchical Superquadric Decomposition with Implicit Space Separation
论文作者
论文摘要
我们介绍了一种新方法，使用一组体积原语（即超Quadrics）重建3D对象。该方法分层将目标3D对象分解为对成对的超季度，从而恢复了更细致的细节。尽管以前已经研究过这种层次结构方法，但我们仅使用预测的超季度的属性引入了一种新的方法来分裂对象空间。该方法在Shapenet数据集上进行了训练和评估。我们的实验结果表明，可以通过针对具有复杂几何形状的各种对象的方法来获得合理的重建。
We introduce a new method to reconstruct 3D objects using a set of volumetric primitives, i.e., superquadrics. The method hierarchically decomposes a target 3D object into pairs of superquadrics recovering finer and finer details. While such hierarchical methods have been studied before, we introduce a new way of splitting the object space using only properties of the predicted superquadrics. The method is trained and evaluated on the ShapeNet dataset. The results of our experiments suggest that reasonable reconstructions can be obtained with the proposed approach for a diverse set of objects with complex geometry.
