Papers for

industrial inspection teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Neural networks recover fiber images despite bending distortions

Proximal-Only Transmission Matrix Recovery of an Arbitrarily Deformed Graded-Index Multimode Fiber

Abstract: The multimode fiber is among the thinnest imaging conduits available, carrying hundreds to thousands of spatial modes through a cross-section comparable to a human hair, but its endoscopic capabilities are currently limited by the sensitivity of the transmission matrix to the fiber's deformed state. Proximal-only recovery of the fiber's transmission matrix is an appealing approach for enabling general use multimode fiber endoscopy, and within the last decade, machine learning techniques have been applied to both single-ended and double-ended transmission matrix recovery tasks. We present a new approach to this interdisciplinary problem and show that neural networks can generalize to recover transmission matrices of an arbitrarily deformed graded-index multimode fiber from proximal measurements alone.

Mon 14 SeptComputer Vision and Pattern Recognition

The gist

Multimode optical fibers can carry many light signals through a very thin strand, like a tiny cable. However, when the fiber bends or deforms, it changes how light passes through, making it hard to use for imaging inside the body. The authors show that neural networks can figure out how the fiber is bending just by looking at signals from one end. This method helps recover clear images even when the fiber is twisted or curved in complex ways.

Open → 2609.14869v1

Point cloud upsampling improves 3D anomaly detection on low-resolution sensors

PC$^2$-AD: Point Cloud Upsampling to Safeguard 3D Anomaly Detection with Resolution-constrained Edge Devices

Abstract: Low-cost and low-resolution sensors used in edge deployments can produce test point clouds that are substantially sparser than the normal training data. This train-test sampling-resolution gap changes the local geometry available to a 3D anomaly detector. We propose PC$^2$-AD, a point cloud upsampling framework that compensates sparse test inputs before downstream detection. Target Domain Candidate Generation (TCG) adapts a pretrained upsampler to normal training geometry and generates a dense candidate pool. Geometry-Aware Candidate Filtering (GACF) selects candidates according to geometric spacing and spatial coverage. Normality-Preserving Point Compensation (NPPC) refines the selection by comparing candidate normality scores with those of their input anchors. The selected points are combined with the unchanged input points and processed by the existing detector. Experiments with six detectors on two Anomaly-ShapeNet settings and Real3D-AD show improvements in the mean of object-level and point-level AUROC for all six detectors in each Anomaly-ShapeNet setting and four on Real3D-AD. These results support point cloud compensation as an input-level approach to improving 3D anomaly detection under low-resolution sensing conditions. Code is publicly available at https://github.com/gyutong406-commits/PC2-AD.

Sun 13 SeptComputer Vision and Pattern Recognition

The gist

Using cheap sensors at the edge can create sparse 3D data that makes it harder to find problems with objects. The authors introduce a method called PC²-AD that adds points to sparse 3D scans before checking for anomalies. Their system chooses extra points carefully to match the original shapes and keep normal parts intact. Tests show this approach helps many existing anomaly detectors work better with low-quality 3D inputs.

Open → 2609.14722v1

Efficient 3D imaging on low-power edge devices speeds up CT scans

An Efficient Out-of-Core Tomographic Imaging Framework for Edge Devices

Abstract: Computed Tomography (CT) is an essential 3D imaging technology widely used in medical diagnostics and scientific research. However, performing CT imaging on edge devices is challenging due to limitations in computational power, memory capacity, and energy budget. This paper presents an efficient CT reconstruction framework, called edgeFBP, designed for Nvidia Jetson System-on-Chip (SoC) devices. edgeFBP adopts an end-to-end pipeline design for efficient out-of-core image reconstruction under tight power and memory constraints. edgeFBP utilizes a mixed-precision strategy leveraging half-precision Tensor Cores (TCs) to accelerate the bottleneck back-projection (BP) kernel. edgeFBP achieves a 1.83x speedup over the widely used RTK library on Jetson Nano and a 2.56x speedup on Jetson AGX. Under a strict 25-Watt power budget, edgeFBP on Jetson Nano achieves up to 5-48x higher energy efficiency than an Nvidia DGX A100, enabling datacenter-scale imaging on constrained edge devices.

Mon 7 SeptDistributed, Parallel, and Cluster Computing

The gist

Computed Tomography (CT) scans create 3D images used in medicine and research but usually require powerful computers. The authors developed a new method called edgeFBP that works well on small, low-power devices like Nvidia Jetson, which have limited memory and processing power. Their approach speeds up the image reconstruction process using special computing units and smart precision choices, making it much faster and more energy efficient compared to current solutions. This allows advanced CT imaging to be done in settings without big data centers.

Open → 2609.07249v1