arXiv subjects

Andy Huynh

Publications and source records attributed to Andy Huynh.

5 recordsLinked to original sources

arXiv2025

Material-informed Gaussian Splatting for 3D World Reconstruction in a Digital Twin

3D reconstruction for Digital Twins often relies on LiDAR-based methods, which provide accurate geometry but lack the semantics and textures naturally captured by cameras. Traditional LiDAR-camera fusion approaches require complex calibration and still struggle with certain materials like glass, which are visible in images but poorly represented in point clouds. We propose a camera-only pipeline that reconstructs scenes using 3D Gaussian Splatting from multi-view images, extracts semantic material masks via vision models, converts Gaussian representations to mesh surfaces with projected material labels, and assigns physics-based material properties for accurate sensor simulation in modern graphics engines and simulators. This approach combines photorealistic reconstruction with physics-based material assignment, providing sensor simulation fidelity comparable to LiDAR-camera fusion while eliminating hardware complexity and calibration requirements. We validate our camera-only method using an internal dataset from an instrumented test vehicle, leveraging LiDAR as ground truth for reflectivity validation alongside image similarity metrics.

cs.CV↗

arXiv2025

Personalizing the meshed SPL/NAC Brain Atlas for patient-specific scientific computing using SynthMorph

Developing personalized computational models of the human brain remains a challenge for patient-specific clinical applications and neuroscience research. Efficient and accurate biophysical simulations rely on high-quality personalized computational meshes derived from patient's segmented anatomical MRI scans. However, both automatic and manual segmentation are particularly challenging for tissues with limited visibility or low contrast. In this work, we present a new method to create personalized computational meshes of the brain, streamlining the development of computational brain models for clinical applications and neuroscience research. Our method uses SynthMorph, a state-of-the-art anatomy-aware, learning-based medical image registration approach, to morph a comprehensive hexahedral mesh of the open-source SPL/NAC Brain Atlas to patient-specific MRI scans. Each patient-specific mesh includes over 300 labeled anatomical structures, more than any existing manual or automatic methods. Our registration-based method takes approximately 20 minutes, significantly faster than current state-of-the-art mesh generation pipelines, which can take up to two hours. We evaluated several state-of-the-art medical image registration methods, including SynthMorph, to determine the most optimal registration method to morph our meshed anatomical brain atlas to patient MRI scans. Our results demonstrate that SynthMorph achieved high DICE similarity coefficients and low Hausdorff Distance metrics between anatomical structures, while maintaining high mesh element quality. These findings demonstrate that our registration-based method efficiently and accurately produces high-quality, comprehensive personalized brain meshes, representing an important step toward clinical translation.

cs.CE↗

arXiv2024

Hexahedral mesh of anatomical atlas for construction of computational human brain models: Applications to modeling biomechanics and bioelectric field propagation

Numerical simulations rely on constructing accurate and detailed models to produce reliable results - a task that is often challenging. This task becomes notably more difficult when the model is of the human brain. We create an anatomically comprehensive hexahedral mesh of the human brain using an open-source digital brain atlas. Digital atlases are valuable tools currently used by medical professionals, medical students, and researchers for gathering, presenting, and discovering knowledge about the human brain. We demonstrate that the atlas can be used to efficiently create an accurate and detailed hexahedral finite element mesh of the brain for scientific computing. We present two case studies. The first case study constructs a biomechanical model of the brain to compute brain deformations and predict traumatic brain injury risk due to violent impact. In the second case study, we construct a bioelectrical model of the brain to solve the electroencephalography (EEG) forward problem, a frequent simulation process used in electrophysiology to study electromagnetic fields generated by the nervous system. We demonstrate efficient and accurate model construction using the meshed anatomical brain atlas, as well as emphasize the importance of effective communication and contextual analysis of results for enabling multi-disciplinary scientific computing research.

cs.CE↗

arXiv2023

Towards Flexibility and Robustness of LSM Trees

Log-Structured Merge trees (LSM trees) are increasingly used as part of the storage engine behind several data systems, and are frequently deployed in the cloud. As the number of applications relying on LSM-based storage backends increases, the problem of performance tuning of LSM trees receives increasing attention. We consider both nominal tunings - where workload and execution environment are accurately known a priori - and robust tunings - which consider uncertainty in the workload knowledge. This type of workload uncertainty is common in modern applications, notably in shared infrastructure environments like the public cloud. To address this problem, we introduce ENDURE, a new paradigm for tuning LSM trees in the presence of workload uncertainty. Specifically, we focus on the impact of the choice of compaction policy, size ratio, and memory allocation on the overall performance. ENDURE considers a robust formulation of the throughput maximization problem and recommends a tuning that offers near-optimal throughput when the executed workload is not the same, instead in a neighborhood of the expected workload. Additionally, we explore the robustness of flexible LSM designs by proposing a new unified design called K-LSM that encompasses existing designs. We deploy our robust tuning system, ENDURE, on a state-of-the-art key-value store, RocksDB, and demonstrate throughput improvements of up to 5x in the presence of uncertainty. Our results indicate that the tunings obtained by ENDURE are more robust than tunings obtained under our expanded LSM design space. This indicates that robustness may not be inherent to a design, instead, it is an outcome of a tuning process that explicitly accounts for uncertainty.

cs.DB↗

arXiv2021

Endure: A Robust Tuning Paradigm for LSM Trees Under Workload Uncertainty

Log-Structured Merge trees (LSM trees) are increasingly used as the storage engines behind several data systems, frequently deployed in the cloud. Similar to other database architectures, LSM trees take into account information about the expected workload (e.g., reads vs. writes, point vs. range queries) to optimize their performance via tuning. Operating in shared infrastructure like the cloud, however, comes with a degree of workload uncertainty due to multi-tenancy and the fast-evolving nature of modern applications. Systems with static tuning discount the variability of such hybrid workloads and hence provide an inconsistent and overall suboptimal performance. To address this problem, we introduce Endure - a new paradigm for tuning LSM trees in the presence of workload uncertainty. Specifically, we focus on the impact of the choice of compaction policies, size-ratio, and memory allocation on the overall performance. Endure considers a robust formulation of the throughput maximization problem, and recommends a tuning that maximizes the worst-case throughput over a neighborhood of each expected workload. Additionally, an uncertainty tuning parameter controls the size of this neighborhood, thereby allowing the output tunings to be conservative or optimistic. Through both model-based and extensive experimental evaluation of Endure in the state-of-the-art LSM-based storage engine, RocksDB, we show that the robust tuning methodology consistently outperforms classical tun-ing strategies. We benchmark Endure using 15 workload templates that generate more than 10000 unique noisy workloads. The robust tunings output by Endure lead up to a 5$\times$ improvement in through-put in presence of uncertainty. On the flip side, when the observed workload exactly matches the expected one, Endure tunings have negligible performance loss.

cs.DB↗