SDFPlane: Explicit Neural Surface Reconstruction of Deformable Tissues

Three-dimensional reconstruction of soft tissues from stereoscopic surgical videos is crucial for enhancing various medical applications. Existing methods often struggle to generate accurate soft tissue geometries or suffer from slow network convergence. To address these challenges, we introduce SDFPlane, an innovative method for fast and precise geometric reconstruction of surgical scenes. This approach efficiently captures scene deformation using a spatial-temporal structure encoder and combines an SDF decoder with a color decoder to accurately model the scene’s geometry and color. Subsequently, we synthesize color images and depth maps with SDF-based volume rendering. Additionally, we implement an error-guided importance sampling strategy, which directs the network’s focus towards areas that are not fully optimized during training. Comparative analysis on multiple public datasets demonstrates that SDFPlane accelerates optimization by over 10 \(\times \) compared to existing SDF-based methods while maintaining state-of-the-art rendering quality. Code is available at https://github.com/IRMVLab/SDFPlane.git .