Multiview Based 3D Scene Understanding On Partial Point Sets
This addresses the challenge of limited field-of-view in commodity cameras for 3D scene understanding, offering an incremental improvement over existing methods.
The paper tackles 3D scene semantic understanding on partial point clouds by proposing a multiview representation approach, resulting in a 31.9% increase in segmentation accuracy for partial scenes and a 4.3% increase for complete scenes.
Deep learning within the context of point clouds has gained much research interest in recent years mostly due to the promising results that have been achieved on a number of challenging benchmarks, such as 3D shape recognition and scene semantic segmentation. In many realistic settings however, snapshots of the environment are often taken from a single view, which only contains a partial set of the scene due to the field of view restriction of commodity cameras. 3D scene semantic understanding on partial point clouds is considered as a challenging task. In this work, we propose a processing approach for 3D point cloud data based on a multiview representation of the existing 360° point clouds. By fusing the original 360° point clouds and their corresponding 3D multiview representations as input data, a neural network is able to recognize partial point sets while improving the general performance on complete point sets, resulting in an overall increase of 31.9% and 4.3% in segmentation accuracy for partial and complete scene semantic understanding, respectively. This method can also be applied in a wider 3D recognition context such as 3D part segmentation.