Tootfinder

AI-based single-shot structured-light depth reconstruction for real-time laparoscopic surgical guidance
Wayne Wonseok Rodgers, Xiangyi Le, Seonghoon Jang, Shuwen Wei, Justin Opfermann, Michael Kam, Axel Krieger, Jin U. Kang
https://arxiv.org/abs/2608.05109 https://arxiv.org/pdf/2608.05109 https://arxiv.org/html/2608.05109
arXiv:2608.05109v1 Announce Type: new
Abstract: Significance. Accurate intraoperative depth perception is important for autonomous and semi-autonomous robotic laparoscopic surgery. Conventional fringe projection profilometry can achieve millimeter-scale accuracy but often requires multi-shot acquisition, digital-micromirror-device projection, and projector-camera synchronization, complicating integration into compact laparoscopic systems.
Aim. To develop a synchronization-free, single-shot depth-sensing platform using a passive LED-illuminated binary mask and a VQ-VAE prior with a custom U-Net depth head.
Approach. A compact projection module was coupled to one channel of a dual-channel laparoscope, while the second channel imaged the fringe-illuminated target. A Zivid 3D camera acquired reference depth for 722 paired phantom images. Zivid depth maps were reprojected into the SSLE image frame for supervised training and evaluation. The VQ-VAE encoded each input into a discrete latent representation, and a latent-space U-Net predicted depth without a separate mask-prediction branch.
Results. Using a fixed train/validation/test split, the proposed model achieved an MAE of 3.70 mm, AbsRel of 0.0326, delta=1.1 accuracy of 0.962, and delta=1.1^2 accuracy of 0.970. It achieved lower MAE than the dual U-Net MaskNet DepthNet baseline and outperformed off-the-shelf monocular depth models in MAE, AbsRel, and threshold accuracy. The pipeline operated at 26.0 Hz over 301 consecutive frames on an NVIDIA A100 GPU.
Conclusions. The LED-illuminated binary-pattern platform with latent-space depth reconstruction enables synchronization-free, video-rate endoscopic depth estimation. Results demonstrate Zivid-referenced phantom reconstruction without an explicit segmentation stage, while emphasizing the importance of dataset size and SSLE-Zivid calibration accuracy.
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from my link log —
mold: a massively parallel linker.
https://arxiv.org/abs/2608.23228
saved 2026-08-27 https://dotat.at/:/USPYY.html

mold: A Massively Parallel Linker
Linking is a critical step in the software build process that combines compiled object files into a single executable or shared library. Despite decades of engineering effort, link times remain a significant bottleneck in the edit-compile-debug cycle, particularly for large C++ programs. Existing linkers exploit limited parallelism, leaving most CPU cores idle during linking. We present mold, a Unix/Linux linker that applies data parallelism systematically across the entire linking pipeline. We…

MR-Compare: A Mixed-Reality Framework for Spatially Grounded Visual Comparison of 3D Gaussian Splatting and Mesh Reconstructions with the Physical Environment
Changrui Zhu, Ernst Kruijff, Pengju Zhang, Simon Julier
https://arxiv.org/abs/2607.20325 https://arxiv.org/pdf/2607.20325 https://arxiv.org/html/2607.20325
arXiv:2607.20325v1 Announce Type: new
Abstract: We introduce MR-Compare, a mixed reality framework for spatially grounded visual comparison between 3D Gaussian splatting and mesh reconstructions with live video see-through (VST). Implemented on a PC-tethered Meta Quest~3, it combines a two-stage registration pipeline with a 3D Slider for cross-media comparison. We evaluated five representative desktop and mobile reconstruction workflows through a real-world benchmark with an exploratory user study ($n=30$) in two static indoor rooms. MR-Compare achieved centimetre-level translation error across all workflows. The two desktop 3DGS workflows showed the strongest overall pattern, with 3DGS-MCMC yielding the lowest registration error and strongest VST-referenced visual consistency. Room-session measures indicated high perceived usability and low workload. We further propose an anisotropy filter, a zero-shot module that leverages Gaussian anisotropies to improve 3DGS registration in MR-Compare. A controlled Replica threshold sweep shows that moderate pruning can improve robustness and reduce residual errors. These results establish system-level feasibility in the tested setting rather than task-level effectiveness or standalone deployment. The project is available at https://github.com/changruizhu96/MR-Compare.
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