FMapping: Factorized Efficient Neural Field Mapping for Real-Time Dense RGB SLAM

In this paper, we introduce FMapping, an efficient neural field mapping framework that facilitates the continuous estimation of a colorized point cloud map in real-time dense RGB SLAM. To achieve this challenging goal without depth, a hurdle is how to improve efficiency and reduce the mapping uncertainty of the RGB SLAM system. To this end, we first build up a theoretical analysis by decomposing the SLAM system into tracking and mapping parts, and the mapping uncertainty is explicitly defined within the frame of neural representations. Based on the analysis, we then propose an effective factorization scheme for scene representation and introduce a sliding window strategy to reduce the uncertainty for scene reconstruction. Specifically, we leverage the factorized neural field to decompose uncertainty into a lower-dimensional space, which enhances robustness to noise and improves training efficiency. We then propose the sliding window sampler to reduce uncertainty by incorporating coherent geometric cues from observed frames during map initialization to enhance convergence. Our factorized neural mapping approach enjoys some advantages, such as low memory consumption, more efficient computation, and fast convergence during map initialization. Experiments on two benchmark datasets show that our method can update the map of high-fidelity colorized point clouds around 2 seconds in real time while requiring no customized CUDA kernels. Additionally, it utilizes x20 fewer parameters than the most concise neural implicit mapping of prior methods for SLAM, e.g., iMAP [ 31] and around x1000 fewer parameters than the state-of-the-art approach, e.g., NICE-SLAM [ 42]. For more details, please refer to our project homepage: https://vlis2022.github.io/fmap/

Additional file 9 of Circulating tumor DNA integrating tissue clonality detects minimal residual disease in resectable non-small-cell lung cancer

Additional file 9. Figure S6: ctDNA testing, LDCT scans, and disease-related events of patients during follow-up periods. . Swimmer plot illustrating the first positive ctDNA testing, the last negative LDCT scans, and pathological events of patients that experienced recurrence or deceased. B). The original and adjusted time intervals between the first positive ctDNA testing and final LDCT scans that detected disease recurrence. Abbreviations: LDCT – low-dose computed tomography, LUAD – lung adenocarcinoma, LUSC - lung squamous-cell carcinoma

Additional file 13 of Circulating tumor DNA integrating tissue clonality detects minimal residual disease in resectable non-small-cell lung cancer

Additional file 13. Supplementary results

Additional file 3 of Circulating tumor DNA integrating tissue clonality detects minimal residual disease in resectable non-small-cell lung cancer

Additional file 3. Table S3: Performance of three strategies