  Efficiency and robustness are increasingly needed for applications on 3D
point clouds, with the ubiquitous use of edge devices in scenarios like
autonomous driving and robotics, which often demand real-time and reliable
responses. The paper tackles the challenge by designing a general framework to
construct 3D learning architectures with SO(3) equivariance and network
binarization. However, a naive combination of equivariant networks and
binarization either causes sub-optimal computational efficiency or geometric
ambiguity. We propose to locate both scalar and vector features in our networks
to avoid both cases. Precisely, the presence of scalar features makes the major
part of the network binarizable, while vector features serve to retain rich
structural information and ensure SO(3) equivariance. The proposed approach can
be applied to general backbones like PointNet and DGCNN. Meanwhile, experiments
on ModelNet40, ShapeNet, and the real-world dataset ScanObjectNN, demonstrated
that the method achieves a great trade-off between efficiency, rotation
robustness, and accuracy. The codes are available at
https://github.com/zhuoinoulu/svnet.
