# BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery arXiv: https://arxiv.org/abs/2312.15679 ยท HTML: https://mlsanity.com/paper/2312.15679/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 51 references that are in the map. - First posted on arXiv 25 Dec 2023. ## Builds on (most cited first, 23 of 23) - [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016) - [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018) - [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016) - [Group-wise Correlation Stereo Network](https://mlsanity.com/paper/1903.04025/) (2019) - [AANet: Adaptive Aggregation Network for Efficient Stereo Matching](https://mlsanity.com/paper/2004.09548/) (2020) - [Hierarchical Deep Stereo Matching on High-resolution Images](https://mlsanity.com/paper/1912.06704/) (2019) - [Stereo Correspondence and Reconstruction of Endoscopic Data Challenge](https://mlsanity.com/paper/2101.01133/) (2021) - [Real-time self-adaptive deep stereo](https://mlsanity.com/paper/1810.05424/) (2018) - [Fast Optical Flow using Dense Inverse Search](https://mlsanity.com/paper/1603.03590/) (2016) - [E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception](https://mlsanity.com/paper/2107.00229/) (2021) - [DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequences](https://mlsanity.com/paper/1908.08918/) (2019) - [MIS-SLAM: Real-time Large Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing](https://mlsanity.com/paper/1803.02009/) (2018) - [SAGE: SLAM with Appearance and Geometry Prior for Endoscopy](https://mlsanity.com/paper/2202.09487/) (2022) - [Dynamic Reconstruction of Deformable Soft-tissue with Stereo Scope in Minimal Invasive Surgery](https://mlsanity.com/paper/2003.10867/) (2020) - [ORBSLAM-based Endoscope Tracking and 3D Reconstruction](https://mlsanity.com/paper/1608.08149/) (2016) - [SLAM based Quasi Dense Reconstruction For Minimally Invasive Surgery Scenes](https://mlsanity.com/paper/1705.09107/) (2017) - [3D Reconstruction of Whole Stomach from Endoscope Video Using Structure-from-Motion](https://mlsanity.com/paper/1905.12988/) (2019) - [Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation](https://mlsanity.com/paper/2005.05050/) (2020) - [Autonomous Scanning for Endomicroscopic Mosaicing and 3D Fusion](https://mlsanity.com/paper/1604.04137/) (2016) - [A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots](https://mlsanity.com/paper/1705.05444/) (2017) - [Real-time Nonrigid Mosaicking of Laparoscopy Images](https://mlsanity.com/paper/2103.07414/) (2021) - [BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching](https://mlsanity.com/paper/2205.03133/) (2022) - [Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery](https://mlsanity.com/paper/2106.07136/) (2021) ## Built on by (most cited first, 2 of 2) - [CudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in real human endoscopy](https://mlsanity.com/paper/2405.16932/) (2024) - [Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation](https://mlsanity.com/paper/2606.23131/) (2026) ## Abstract (arXiv) Purpose: Common dense stereo Simultaneous Localization and Mapping (SLAM) approaches in Minimally Invasive Surgery (MIS) require high-end parallel computational resources for real-time implementation. Yet, it is not always feasible since the computational resources should be allocated to other tasks like segmentation, detection, and tracking. To solve the problem of limited parallel computational power, this research aims at a lightweight dense stereo SLAM system that works on a single-core CPU and achieves real-time performance (more than 30 Hz in typical scenarios). Methods: A new dense stereo mapping module is integrated with the ORB-SLAM2 system and named BDIS-SLAM. Our new dense stereo mapping module includes stereo matching and 3D dense depth mosaic methods. Stereo matching is achieved with the recently proposed CPU-level real-time matching algorithm Bayesian Dense Inverse Searching (BDIS). A BDIS-based shape recovery and a depth mosaic strategy are integrated as a new thread and coupled with the backbone ORB-SLAM2 system for real-time stereo shape recovery. Results: Experiments on in-vivo data sets show that BDIS-SLAM runs at over 30 Hz speed on modern single-core CPU in typical endoscopy/colonoscopy scenarios. BDIS-SLAM only consumes around an additional 12% time compared with the backbone ORB-SLAM2. Although our lightweight BDIS-SLAM simplifies the process by ignoring deformation and fusion procedures, it can provide a usable dense mapping for modern MIS on computationally constrained devices. Conclusion: The proposed BDIS-SLAM is a lightweight stereo dense SLAM system for MIS. It achieves 30 Hz on a modern single-core CPU in typical endoscopy/colonoscopy scenarios (image size around 640*480). BDIS-SLAM provides a low-cost solution for dense mapping in MIS and has the potential to be applied in surgical robots and AR systems.