SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
Journal:
arXiv
Published Date:
Jun 29, 2025
Abstract
In contemporary surgical research and practice, accurately comprehending 3D
surgical scenes with text-promptable capabilities is particularly crucial for
surgical planning and real-time intra-operative guidance, where precisely
identifying and interacting with surgical tools and anatomical structures is
paramount. However, existing works focus on surgical vision-language model
(VLM), 3D reconstruction, and segmentation separately, lacking support for
real-time text-promptable 3D queries. In this paper, we present SurgTPGS, a
novel text-promptable Gaussian Splatting method to fill this gap. We introduce
a 3D semantics feature learning strategy incorporating the Segment Anything
model and state-of-the-art vision-language models. We extract the segmented
language features for 3D surgical scene reconstruction, enabling a more
in-depth understanding of the complex surgical environment. We also propose
semantic-aware deformation tracking to capture the seamless deformation of
semantic features, providing a more precise reconstruction for both texture and
semantic features. Furthermore, we present semantic region-aware optimization,
which utilizes regional-based semantic information to supervise the training,
particularly promoting the reconstruction quality and semantic smoothness. We
conduct comprehensive experiments on two real-world surgical datasets to
demonstrate the superiority of SurgTPGS over state-of-the-art methods,
highlighting its potential to revolutionize surgical practices. SurgTPGS paves
the way for developing next-generation intelligent surgical systems by
enhancing surgical precision and safety. Our code is available at:
https://github.com/lastbasket/SurgTPGS.
