# Spatial-Division Augmented Occupancy Field for Bone Shape Reconstruction from Biplanar X-Rays

arXiv: https://arxiv.org/abs/2407.15433 · HTML: https://mlsanity.com/paper/2407.15433/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2024.

## Built on by (most cited first, 2 of 2)

- [MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images](https://mlsanity.com/paper/2412.09402/) (2024)
- [Cross-view Generalized Diffusion Model for Sparse-view CT Reconstruction](https://mlsanity.com/paper/2508.10313/) (2025)

## Abstract (arXiv)

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