  In this paper, we propose binary sparse convolutional networks called BSC-Net
for efficient point cloud analysis. We empirically observe that sparse
convolution operation causes larger quantization errors than standard
convolution. However, conventional network quantization methods directly
binarize the weights and activations in sparse convolution, resulting in
performance drop due to the significant quantization loss. On the contrary, we
search the optimal subset of convolution operation that activates the sparse
convolution at various locations for quantization error alleviation, and the
performance gap between real-valued and binary sparse convolutional networks is
closed without complexity overhead. Specifically, we first present the shifted
sparse convolution that fuses the information in the receptive field for the
active sites that match the pre-defined positions. Then we employ the
differentiable search strategies to discover the optimal opsitions for active
site matching in the shifted sparse convolution, and the quantization errors
are significantly alleviated for efficient point cloud analysis. For fair
evaluation of the proposed method, we empirically select the recently advances
that are beneficial for sparse convolution network binarization to construct a
strong baseline. The experimental results on Scan-Net and NYU Depth v2 show
that our BSC-Net achieves significant improvement upon our srtong baseline and
outperforms the state-of-the-art network binarization methods by a remarkable
margin without additional computation overhead for binarizing sparse
convolutional networks.
