OctNet: Learning Deep 3D Representations at High Resolutions

TLDR

OctNet is a representation for deep learning with sparse 3D data. OctNet exploits input sparsity by hierarchically partitioning space with unbalanced octrees, storing pooled features at leaf nodes, and focusing memory and computation on dense regions to enable deeper networks. OctNet enables deep, high‑resolution 3D convolutional networks, improves memory efficiency, and improves performance on tasks such as object classification, orientation estimation, and point‑cloud labeling.

Abstract

We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.