Some more #dNoize wip

Please tell me that "Approve for me," that @OpenAI promised not to drain tokens anymore, is not a reason for last week's token drainings?🤔 I just found that I enabled it almost everywhere after those promises.

Can a Display Be Used for Rapid LiDAR-Camera Extrinsic Calibration? Zuhao Zou, Xiangcheng Hu, Jianhao Jiao, Jin Wu Paper: arxiv.org/abs/2610.28571 Code (GPL3): github.com/ZuhaoZou2026/Scre… Abstract: ScreenCalib enables light detection and ranging (LiDAR)--camera extrinsic calibration using an ordinary programmable display, providing known visual markers and controlled geometry while reducing dedicated target preparation. The camera observes displayed AprilTags, whereas the LiDAR measures the physical screen surface and boundary, requiring indirect cross-modal association. Display geometry introduces association ambiguities, while indoor clutter complicates extraction. ScreenCalib addresses these challenges through tag-guided display extraction, geometric hypothesis generation and refinement, and surrounding scene edges for ambiguity resolution. The single-pose mode requires only one static placement. The joint multi-pose mode combines independent static observations through cross-pose consensus and jointly refines the shared extrinsics and tag scale, reducing sensitivity to tag-size measurement errors without requiring continuous motion or inter-pose trajectory estimation. Experiments in simulation and on three different sensor platforms cover mechanical and nonrepetitive LiDARs in clean and cluttered environments. Benchmark results show that ScreenCalib outperforms competitive calibration-board-based methods in translation accuracy. Joint multi-pose refinement further reduces mean rotation and translation errors across the three platforms and improves rendered-map alignment. Evaluation across readily available displays, including cell phones, tablets, laptops, computer monitors, and televisions, further examines ScreenCalib's versatility as a practical alternative to dedicated calibration targets.

Gerchberg-Saxton Optimized Silicon Metasurface for Sinusoidal Structured-Light Three-Dimensional Imaging Gengyuan Ye, Shuming Wang arxiv.org/abs/2610.26956 Abstract: Three-dimensional (3D) optical imaging based on structured illumination has attracted considerable attention in applications including machine vision, robotics, and computational sensing. However, conventional structured-light systems generally rely on bulky projection components, such as digital micromirror devices and diffractive optical elements, which limit system miniaturization and integration. Here, we demonstrate a compact structured-light 3D imaging platform enabled by a silicon dielectric metasurface operating at a near-infrared wavelength of 940 nm. The desired sinusoidal fringe illumination is encoded into the metasurface through a Gerchberg-Saxton (GS) phase optimization algorithm, which converts the target far-field distribution into a nanoscale phase profile and subsequently into silicon nanopillar geometries. The fabricated metasurface generates high-quality sinusoidal structured illumination, which is integrated with a four-step phase-shifting reconstruction method to recover 3D object profiles. The proposed approach provides a compact and scalable solution for miniaturized structured-light projection and optical 3D sensing.

A Unified Theory of Image Scaling: Point Spread Functions, Discrete Kernels, and Scale-Spaces Assaf Shocher (Technion – Israel Institute of Technology) arxiv.org/abs/2610.22472 Abstract: We derive the optically aligned system of image-scaling operators Ks that reproduces optical zoom (exact zoom-out, canonical zoom-in), by identifying a Huygens-like consistency principle in which each pixel acts as a secondary source. The system is determined in closed form by the camera's point spread function (PSF), itself the operator K0. The operators compose, Ks′∘Ks = Kss′ (with band projection for down-up), and account for both upscaling and downscaling, which form a Moore-Penrose pseudoinverse pair, K1/s = K†s. We show that image scaling, as routinely practiced, is neither optically faithful nor internally consistent; for example, common anti-aliasing rules generally correspond to no camera, and two resizes by a factor of 1/2 generally do not equal one resize by a factor of 1/4. Gaussian scale-space kernels cannot be exact finite-aperture PSFs. Gaussian scale-space is an idealized special case of what emerges here: a generalized scale-space for sampled images, derived entirely from camera optics, bidirectional, and not limited to Gaussian kernels. The system goes beyond scaling and supports composable and invertible subpixel geometric transformations. We empirically validate its optical predictions with real captures and noisy optics simulations, and demonstrate its practical impact on the stability of tasks such as super-resolution and classification.

Energy-minimized interpolatory B-spline lofting for sequences of closed contours Shutao Tang, Zixuan Zhao arxiv.org/abs/2610.24569 Abstract: B-spline lofting through closed cross-sections is common in reverse engineering, but interpolating each section on its own knot vector can produce an unnecessarily large control net. We construct a common-knot method for closed section curves of odd and even degree by minimizing the strain energy of each section subject to its interpolation constraints. Feasibility for closed section data and uniqueness of the resulting saddle-point solution require full row rank of the periodic collocation matrices, a property that a previously proposed periodic Schoenberg-Whitney window condition does not always guarantee. We establish the exact parity distinction: the condition is sufficient at odd point counts but admits singular configurations at every admissible even point count. For the odd and even degrees used here, the natural and shifting anchor knots are nonsingular; at odd point counts the window condition certifies the selected candidate, whereas at even point counts a rank test with an anchor fallback is applied. The lofting method therefore retains a nonsingular square subsystem for each section and uses nestedness to transfer full row rank to the rectangular common-knot system. Across six closed-contour models the common-knot method uses 83-98% fewer control points than traditional interpolating lofting, with section energies no larger than those of a previous knot-selection method within numerical precision.

llmon - added opt-in stats for multiple local network PCs. github.com/ssh4net/llmon Still only Claude and Codex.

Dear @apple I’m an idiot but you are genius. Please in a simple form explain me how image captured with native iPhone camera, required, fucking 10sec, to just load for the edits?