  Retrieving 3D bone anatomy from biplanar X-ray images is crucial since it can
significantly reduce radiation exposure compared to traditional CT-based
methods. Although various deep learning models have been proposed to address
this complex task, they suffer from two limitations: 1) They employ voxel
representation for bone shape and exploit 3D convolutional layers to capture
anatomy prior, which are memory-intensive and limit the reconstruction
resolution. 2) They overlook the prevalent occlusion effect within X-ray images
and directly extract features using a simple loss, which struggles to fully
exploit complex X-ray information. To tackle these concerns, we present
Spatial-division Augmented Occupancy Field~(SdAOF). SdAOF adopts the continuous
occupancy field for shape representation, reformulating the reconstruction
problem as a per-point occupancy value prediction task. Its implicit and
continuous nature enables memory-efficient training and fine-scale surface
reconstruction at different resolutions during the inference. Moreover, we
propose a novel spatial-division augmented distillation strategy to provide
feature-level guidance for capturing the occlusion relationship. Extensive
experiments on the pelvis reconstruction dataset show that SdAOF outperforms
state-of-the-art methods and reconstructs fine-scale bone surfaces.The code is
available at https://github.com/xmed-lab/SdAOF
