arXiv subjects

Soroush Khoubyarian

Publications and source records attributed to Soroush Khoubyarian.

5 recordsLinked to original sources

arXiv2026

RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression

Holography offers significant potential for AR/VR applications. However, its adoption is limited by the high demand for data compression. Existing deep learning approaches generally lack rate adaptivity within a single network and often require multiple models to cover different bandwidth requirements. We present RAVQ-HoloNet, a rate-adaptive vector quantization framework that integrates the rate-adaptive compression with the transformation of image data into phase-only hologram. RAVQ-HoloNet achieves high-fidelity reconstructions, outperforming current state-of-the-art methods implemented via two distinct architectural configurations: a standard model optimized for low bit rates and a deeper, extended variant tailored for ultra low bit rate setting. To evaluate these models, we utilized the DIV2K dataset as a benchmark for high-fidelity holographic reconstruction. Quantitative analysis in the simulation reveals that our approach significantly surpasses current benchmarks. Specifically, in the low bit rate domain, our method achieves a BD-Rate reduction of -33.91% and a BD-PSNR gain of 1.02dB relative to the state-of-the-art method. Additionally, experimental results on the SLM device show that our method achieves higher contrast and improved quality.

cs.LG↗

arXiv2025

Correcting astigmatism and ellipticity in Gaussian beams using a cylindrical lens pair with tunable focal lengths

Correcting astigmatism and ellipticity in laser beams is critical for improving performance in many applications like microscopy, atomic physics, quantum information processing, and advanced manufacturing. Passive correction methods based on cylindrical lens telescopes require choosing lenses with precise focal lengths, effectively limiting the range of tunability when using standard catalog optics. Active solutions based on diffractive optical elements can achieve superior performance, but they are bulky, expensive, and suffer from finite diffraction efficiency and added complexity. Here, we introduce a simple method to convert astigmatic elliptical beams into circular Gaussian beams without astigmatism. Our method comprises three cylindrical lenses. The first lens focuses the beam along its major axis to create a plane where the intensity profile is radially symmetric. The second and third lenses are placed one behind the other in that plane at a relative angle, acting as a biaxial lens pair with tunable focal lengths. By adjusting the relative angle of the lenses, the two separate beam waists of the astigmatic beam can be overlapped, resulting in a circular Gaussian beam without astigmatism. We theoretically validate our method, numerically quantify its robustness to experimental imperfections, and experimentally demonstrate its ability to circularize the output beam of a commercial laser source. Our method corrects astigmatism and ellipticity in laser beams without requiring precise focal length matching, offering greater flexibility than other passive solutions and greater cost-effectiveness than active methods. Its simple and compact design makes it well suited for integration into both tabletop optical setups and industrial systems.

physics.optics↗

arXiv2023

Riemannian Optimization for Distance-Geometric Inverse Kinematics

Solving the inverse kinematics problem is a fundamental challenge in motion planning, control, and calibration for articulated robots. Kinematic models for these robots are typically parametrized by joint angles, generating a complicated mapping between the robot configuration and the end-effector pose. Alternatively, the kinematic model and task constraints can be represented using invariant distances between points attached to the robot. In this paper, we formalize the equivalence of distance-based inverse kinematics and the distance geometry problem for a large class of articulated robots and task constraints. Unlike previous approaches, we use the connection between distance geometry and low-rank matrix completion to find inverse kinematics solutions by completing a partial Euclidean distance matrix through local optimization. Furthermore, we parametrize the space of Euclidean distance matrices with the Riemannian manifold of fixed-rank Gram matrices, allowing us to leverage a variety of mature Riemannian optimization methods. Finally, we show that bound smoothing can be used to generate informed initializations without significant computational overhead, improving convergence. We demonstrate that our inverse kinematics solver achieves higher success rates than traditional techniques, and substantially outperforms them on problems that involve many workspace constraints.

cs.RO↗

arXiv2021

Certifiably Optimal Monocular Hand-Eye Calibration

Correct fusion of data from two sensors is not possible without an accurate estimate of their relative pose, which can be determined through the process of extrinsic calibration. When two or more sensors are capable of producing their own egomotion estimates (i.e., measurements of their trajectories through an environment), the 'hand-eye' formulation of extrinsic calibration can be employed. In this paper, we extend our recent work on a convex optimization approach for hand-eye calibration to the case where one of the sensors cannot observe the scale of its translational motion (e.g., a monocular camera observing an unmapped environment). We prove that our technique is able to provide a certifiably globally optimal solution to both the known- and unknown-scale variants of hand-eye calibration, provided that the measurement noise is bounded. Herein, we focus on the theoretical aspects of the problem, show the tightness and stability of our solution, and demonstrate the optimality and speed of our algorithm through experiments with synthetic data.

cs.RO↗

arXiv2020

Inverse Kinematics for Serial Kinematic Chains via Sum of Squares Optimization

Inverse kinematics is a fundamental problem for articulated robots: fast and accurate algorithms are needed for translating task-related workspace constraints and goals into feasible joint configurations. In general, inverse kinematics for serial kinematic chains is a difficult nonlinear problem, for which closed form solutions cannot be easily obtained. Therefore, computationally efficient numerical methods that can be adapted to a general class of manipulators are of great importance. % to motion planning and workspace generation tasks. In this paper, we use convex optimization techniques to solve the inverse kinematics problem with joint limit constraints for highly redundant serial kinematic chains with spherical joints in two and three dimensions. This is accomplished through a novel formulation of inverse kinematics as a nearest point problem, and with a fast sum of squares solver that exploits the sparsity of kinematic constraints for serial manipulators. Our method has the advantages of post-hoc certification of global optimality and a runtime that scales polynomialy with the number of degrees of freedom. Additionally, we prove that our convex relaxation leads to a globally optimal solution when certain conditions are met, and demonstrate empirically that these conditions are common and represent many practical instances. Finally, we provide an open source implementation of our algorithm.

cs.RO↗