A new AI technique named xvr enables patient‑specific use of X‑ray imaging for surgical navigation, potentially improving the safety and precision of minimally invasive procedures. The method is applicable in fields such as orthopedics and neurosurgery, where accurate guidance is critical.
We present a reproducibility study of XFeat, a lightweight local feature extractor and matcher designed to identify corresponding points across images efficiently on resource-constrained hardware. We re-implement the architecture based on the paper and supplementary material, re-evaluate the authors' released checkpoint alongside our re-implementation, and conduct additional architectural ablations to examine design choices that were not fully justified in the original work.
Recovering high dynamic range (HDR) radiance from a single standard dynamic range (SDR) image is highly ill-posed. Extreme luminance variation and severe quantization in dark regions make accurate reconstruction challenging, often leading to visual artifacts and color distortions.
arXiv:2608.29269v1 Announce Type: new Abstract: Relightable interactive scene reconstruction aims to build an editable 3D model from scans of different object arrangements and render new layouts unde...
arXiv:2607. 14287v1 Announce Type: cross Abstract: Defect segmentation in additive manufacturing (AM) X-ray computed tomography (XCT) images remains challenging due to severe class imbalance and large distribution shifts across scan conditions.
The paper introduces STEMPix, a phase‑transition‑material‑based pixel that generates a 3‑bit local edge direction code (LEDC) by combining temporal change and spatial edge information within a CMOS‑compatible image sensor array. The design separates the photodiode and computation layers to maintain light‑collection efficiency while adding in‑array processing circuitry, achieving a 1.73 µm horizontal pitch, 2.36 µm vertical pitch, and 95.47% fill factor with an average switching energy of 0.465 fJ per LEDC operation. STEMPix supports global‑shutter capture and dynamic thresholding, offering a compact, scalable front‑end for edge‑movement‑aware sensing systems.
arXiv:2606. 22574v2 Announce Type: replace-cross Abstract: While synthetic data generation resolves the manual labeling bottleneck in computer vision, minimizing the syn-to-real domain gap requires optimizing rendering variables.
arXiv:2608.29043v1 Announce Type: new Abstract: Light-effect contamination poses a significant challenge to nighttime visibility enhancement. Most methods suppress light effects by estimating and dec...
arXiv:2608. 09519v1 Announce Type: cross Abstract: We present a reproducibility study of XFeat, a lightweight local feature extractor and matcher designed to identify corresponding points across images efficiently on resource-constrained hardware.