LGS: A Light-weight 4D Gaussian Splatting for Efficient Surgical Scene Reconstruction

Project Page | ArXiv Paper

Accepted by International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024 poster)

Hengyu Liu^*, Yifan Liu^*, Chenxin Li^*, Wuyang Li, Yixuan Yuan^✉

The Chinese University of Hong Kong

^* Equal Contribution, ^✉ Corresponding Author.

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💡Highlight

• We propose a holistic Lightweight 4D Gaussian Splatting (LGS) framework that allows for achieving satisfactory endoscopic reconstruction with both efficient rendering and storing.
• We present a Deformation-Aware Pruning (DAP) which alleviates the Quantity burden of Gaussian representation.
• We propose a Gaussian-Attribute Pruning (GAP), which addresses the High-dimension burden of Gaussian attributes.
• We present Feature Field Condensation (FFC) which mitigates the High-resolution burden of spatial-temporal deformable fields.
• Experimental results show that LGS can achieve higher storage efficiency with an over $9\times$ compression rate, whilst maintaining pleasing reconstruction quality and rendering speed.