  Existing 3D occupancy networks demand significant hardware resources,
hindering the deployment of edge devices. Binarized Neural Networks (BNN) offer
substantially reduced computational and memory requirements. However, their
performance decreases notably compared to full-precision networks. Moreover, it
is challenging to enhance the performance of binarized models by increasing the
number of binarized convolutional layers, which limits their practicability for
3D occupancy prediction. To bridge these gaps, we propose a novel binarized
deep convolution (BDC) unit that effectively enhances performance while
increasing the number of binarized convolutional layers. Firstly, through
theoretical analysis, we demonstrate that 1 \times 1 binarized convolutions
introduce minimal binarization errors. Therefore, additional binarized
convolutional layers are constrained to 1 \times 1 in the BDC unit. Secondly,
we introduce the per-channel weight branch to mitigate the impact of
binarization errors from unimportant channel features on the performance of
binarized models, thereby improving performance while increasing the number of
binarized convolutional layers. Furthermore, we decompose the 3D occupancy
network into four convolutional modules and utilize the proposed BDC unit to
binarize these modules. Our BDC-Occ model is created by applying the proposed
BDC unit to binarize the existing 3D occupancy networks. Comprehensive
quantitative and qualitative experiments demonstrate that the proposed BDC-Occ
is the state-of-the-art binarized 3D occupancy network algorithm.
