United States

NAUTICAL will establish a workforce development strategy, a competency development model, and a series of courses to build upon the professional cadre scientists, engineers, and technicians able to develop and advance artificial intelligence (AI) at NIWC Pacific and other warfare. The demand for an effective, capable AI workforce is greater than ever and will continue to grow because the world appears to be at the cusp of an AI revolution.  This project will focus on providing students with basic AI algorithmic theory and more practical AI implementation skills from a DoD perspective while limiting the mathematical aspects of AI. 
 
NAUTICAL will provide different sessions for basic, intermediate and advanced levels of AI coursework, developed primarily by NIWC AI SMEs for the Navy and DoD.  Basic AI for DoD 101 training will be conducted in multiple modules over the course of one to two days consisting of a series of lessons and programming assignments to cover various fundamental topics in the world of AI. Intermediate and advanced focus courses in specific stand-alone AI technology area will be provided in modules following the novice level lectures/exercises. Courses will be held virtually via Flankspeed Teams to provide flexibility and recordability for posterity.  
 
NAUTICAL will publicize our workforce development effort throughout NIWC Pacific and other Naval warfare centers. All DoD military, civilian, and contractors are welcome to partake in NAUTICAL.   
 
Guest lecturers will be recruited to provide lectures in a variety of AI topics that our local AI SME’s may not be knowledgeable about.  
 
Classes are planned to be structured and based off of the textbook Artificial Intelligence: A modern Approach by Stuart Russell and Peter Norvig. This textbook is nicknamed the “Bible for AI” by Doug Lange (NIWC PAC SSTM AI) and has been adopted by over 1500 universities/colleges for their AI curriculum.  

Novice-level topics planned to be covered include: 
Supervised/Unsupervised Learning 
Deep learning 
Computer vision 
Natural Language Processing (NLP) 
Reinforcement Learning, Q-Learning 
Multi-Agent Reinforcement Learning (MARL) 
Automated Planning/PDDL (Planning Domain Definition Language) 
Adversarial Planning, MCTS (Monte Carlo Tree Search), AlphaZero 
Probabilistic Reasoning 
Robotics 
Explainable AI 
AI Dangers/Ethics 

Whenever possible, following each of the lecture modules will be a Google Colab laboratory exercise for students to work on directly with help from the instructor and NAUTICAL team.  

NAML 2026

Learn AI with NAUTICAL

lab exercises

workforce development

artificial intelligence

### Welcome to the 9th annual workshop on Naval Applications of Machine Learning!

NAML is the premier annual event showcasing current machine learning research relevant to Naval applications. We welcome participation from the defense community, industry, and academia. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. 
 Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications
* To connect machine learning researchers with experts in military requirements
* To build and strengthen collaborations within the defense research community

#### *"What we do as a Navy is a human business. Don’t pass up an opportunity to come together as a team. Don’t pass up leadership opportunities, and don’t outsource them to a PowerPoint. If you have an opportunity to come together as a team, to increase belonging, do it."*
Admiral John M. Richardson, 31st Chief of Naval Operations

Please log in to explore this event. You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

Registration for this workshop has now closed. We appreciate your interest and hope to welcome you at a future event. If you’d like to hear about similar upcoming events, please check our website or join our mailing list.

Registration for this workshop has now closed.

NAML 2025

The 9th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.

poster

### Welcome to the 10th annual workshop on Naval Applications of Machine Learning!

NAML is the premier annual event showcasing current machine learning research relevant to Naval applications. We welcome participation from the defense community, industry, and academia. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. 
Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.
#### On-Demand Access

* This is an on-demand site and all the recorded content from the workshop is available here. 
* Access to the recordings is exclusively available to registered participants.
* Each registrant has received an email notification, with instructions on how to access the content.

[![](https://assets.underline.io/markdown_image/1/image/60cd12aa376c191d3467ea2ee60944ea.png)](https://naml2026.org/)

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications
* To connect machine learning researchers with experts in military requirements
* To build and strengthen collaborations within the defense research community

#### *"What we do as a Navy is a human business. Don’t pass up an opportunity to come together as a team. Don’t pass up leadership opportunities, and don’t outsource them to a PowerPoint. If you have an opportunity to come together as a team, to increase belonging, do it."*
Admiral John M. Richardson, 31st Chief of Naval Operations

This is an on-demand site. All recorded content from NAML 2026 is available here and access to it is exclusively available to registered participants.

**Please log in using the email address you registered with.** If you have any issues logging in, please contact us at <naml2026@underline.io>.

Let’s celebrate the past decade by investing in the next. 🚀

This is an on-demand site. All recorded content from NAML 2026 is available here and access to it is exclusively available to registered participants.

**Explore the latest advancements in machine learning tailored to Naval applications 🔎**

With on-demand access to NAML 2026, you can watch all lectures & browse poster presentations at your convenience, while also engaging directly with presenters via the Underline platform - ask questions, exchange ideas, and stay connected.

**If you still haven’t registered, you can do so now.**
👇
**Take advantage of a special one-time offer: full access for just $99, with complimentary registration for students, government, and military employees.**

**Get access here: https://naml2026-ondemand.paperform.co/**

Please check your spam folder, or contact our support team at **<naml2026@underline.io>** if you registered, but still haven't received an access email.

Let’s celebrate the past decade by investing in the next. 🚀

Please register!

The 10th Annual Workshop on Naval Applications of Machine Learning (NAML 2026) was held in San Diego, California, March 2-5, 2026. NAML is a forum for interdisciplinary experts from government, industry, and academia to discuss results of ongoing research on theory and applications of machine learning to address some of the most challenging problems faced by our naval forces. 



We propose a novel method to estimate the pose of a camera relative to a target of interest using only two keypoint features. This addresses a limitation of classical computer vision methods and many state-of-the-art techniques, which require at least three keypoint features to return a unique solution. Our method can be implemented with a monocular camera using information from camera imagery, intrinsics, and extrinsics like its rotation and altitude. This set of known parameters enables us to derive a unique solution for the camera pose using a trigonometry-based approach. In this work, we use a U.S. Naval ship as our target of interest in a simulated dataset. To show generalizability of our method, we test it against an open source 6D pose dataset containing several household items. We compare the performance of our method against various Perspective-n-Point (PnP) methods on these datasets to evaluate for robustness and accuracy. By matching performance of existing PnP methods on the 6D pose dataset and outperforming them on the maritime synthetic dataset, we have demonstrated that our method is effective, robust, and versatile for use in real world applications.

P2P-Trig: An Efficient 2-Point Trigonometric Solution for Camera Pose Using Known Camera Parameters

Quantum-Resistant Endpoint Security for U.S. Navy Document Integrity
HP is pioneering quantum-resistant endpoint security to protect sensitive U.S. Navy documents from emerging quantum computing threats. Their technology embeds NIST-standard post-quantum cryptographic algorithms into proprietary hardware, including the 5th generation Endpoint Security Controller (ESC) for PCs and custom ASIC chips for printers. This hardware-level protection secures BIOS integrity and device boot processes, preventing firmware attacks from even quantum-enabled adversaries.
By proactively embedding quantum-resistant cryptography, HP mitigates risks like "Harvest Now, Decrypt Later," ensuring Navy documents and communications remain confidential and authentic throughout their lifecycle. The solution also supports secure firmware upgrades, complying with federal mandates such as the Quantum Computing Cybersecurity Preparedness Act and the NIST 2030 PQC migration deadline. This approach future-proofs endpoints with long refresh cycles, affording robust protection for classified and controlled unclassified information.
HP’s poster will showcase their quantum-resistant endpoint security designed to protect U.S. Navy documents from quantum threats. It will highlight their proprietary hardware—5th generation Endpoint Security Controller in PCs and custom ASICs in printers—using NIST-standard post-quantum cryptography to secure BIOS and device boot processes.
In summary, HP's quantum-resistant PCs and printers deliver resilient endpoint security essential for safeguarding Navy mission-critical data and national security against next-generation quantum threats


Quantum-Resistant Endpoint Security for U.S. Navy Document Integrity

Warfighter readiness is essential to the Navy’s success, yet a standardized method for predicting individual readiness status is currently lacking. Data-driven machine learning applications have innovated prognostic algorithms for human disease risk and complex biological states [1]. Leveraging this approach, we built a predictive model of readiness using physical (e.g., heart rate, blood pressure), psychological (e.g., leadership satisfaction), and genetic (e.g., stress‑related variants) measures from a sample of warfighters (N = 196). Readiness was measured using a validated 15-item Military Readiness Scale (MRS-15 [2]). Individuals were dichotomized into “high” (n = 92) and “low” (n = 104) readiness status using a MRS-15 cutoff score of 77 out of 100 (equivalent to a readiness level of 77%). As we have previously reported, we used Random Forest and Least Absolute Shrinkage and Selection Operator (LASSO) regression models as a baseline. For the present evaluation, we added Support Vector Machine (SVM) and K-Nearest-Neighbors (KNN) models for comparison, since they are purported to excel on maximizing generalizability given high-dimensional data with minimal data samples [3]. On average, we found a 12% increase in the variance explained (R2) with all 289 features (R2 = 0.41) as compared to 68 hand-selected features (R2 = 0.29); however, the contribution of the number of features on classification accuracy was negligible. Therefore, relatively high classification accuracies (~70%) may be obtained using fewer features, in this dataset. Inclusion of the genetic variants with all features appeared to improve classification accuracy by a maximum of 1-2%, across models. SVM and KNN models achieved their highest performance using the full feature set, whereas Random Forest and LASSO performed best using fewer features. Overall, this application of machine learning to analyze warfighter data can improve insight into Fleet readiness, leading to faster, better decisions.

[1] Ho DSW, Schierding W, Wake M, Saffery R, O’Sullivan J. Machine learning SNP based prediction for precision medicine. Front Genet. 2019;10:267. https://doi.org/10.3389/fgene.2019.00267
[2] Taylor MK, Venkatesh A, Campbell JS, Heaton KJ, Kershaw RT, Gibbs SH, Hernández LM. Development and Validation of the Military Readiness Scale in US Naval Aircrew. Military Medicine. 2025 Nov-Dec;190(11-12):e2420-2426. https://doi.org/10.1093/milmed/usaf193
[3] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12 (85):2825-30. https://jmlr.org/papers/v12/pedregosa11a.html

Machine Learning Applications to Predict Military Readiness

Accurate recognition and tracking of human instances captured at a distance in various poses, particularly those in distress during chaotic events, is crucial for various applications, including disaster relief, damage assessment, and rapid distress-aware triage in battlefield scenarios. These applications have been increasingly empowered by the recent evolution of AI technologies integrated into unmanned platforms, such as UGVs and UAVs. Particularly, identification of injured or distressed individuals via pose analysis is a crucial component of these applications in diverse real-world environments. However, pose identification on video feeds from unmanned systems faces significant challenges due to adverse conditions, such as platform vibration-induced noise, low camera resolution, and camera pose uncertainty. Additionally, data scarcity in the human pose domain also becomes a major bottleneck to achieving successful pose identification. To tackle the above adverse conditions, the Army-DTRA collaborative project called Archangel currently aims to advance the science behind pose/casualty identification by leveraging the multimodal LLM (MLLM)-based scalable framework for automatically generating pose transition descriptions for Composed Pose Retrieval (CPR) benchmarks. Archangel is a customer-reimbursed program conducted by the DEVCOM Army Research Laboratory (ARL) and sponsored by the Defense Threat Reduction Agency (DTRA). Archangel aims to develop advanced AI models integrated into small platforms to recognize human instances in distress primarily for rapid distress-aware triage in chaotic events. Archangel started in early 2020 and is divided into two phases: Phase 1 and 2. Phase 1 ended in 2023 and the deliverables include enhanced machine learning models for detecting human instances in various poses captured by UAVs flying at relatively high altitudes and long ranges. Archangel is currently in Phase 2, focusing on identifying individuals in distress in the context of disastrous events or military conflicts to prioritize the search and rescue of victims or Warfighters in severe physiological conditions. In this workshop, we will introduce our pose identification framework called AutoComPose, the first MLLM-based framework for automatically generating pose transition descriptions that eliminates the need for costly human annotations and enhancing CPR accessibility. AutoComPose is dataset-agnostic, integrating seamlessly with any human image dataset. In this work, we constructed two benchmarks AIST-CPR and PoseFixCPR, as new testbeds for CPR evaluation. Beyond CPR, we will also demonstrate MLLMs’ potential for structured motion understanding, reducing annotation costs while improving scalability. Finally, we will highlight how our MLLM-based pose retrieval establishes the foundation for detecting casualties crucial for distress-aware triage.

Human Pose Identification Using Multimodal LLMs for Rapid Distress-Aware Triage

Sound transmission in the shallow ocean is highly dependent on the characteristics of the sea floor. An effective seabed, suitable for long-range transmission loss modeling, can be predicted with machine learning using passive sonar. Specifically, spectrograms of passing ships of opportunity (SOO) have been used in a ResNet-18 algorithm to classify an effective seabed type. One challenge with this approach is defining a catalog of seabed classes that are acoustically distinct. Currently, porosity is hypothesized to be the most significant parameter of the seabed; for the current work a catalog is defined with 15 seabed classes based on increasing porosity. This catalog is used to generate synthetic SOO spectrograms to train ResNet-18 to predict the seabed class. The trained algorithms are tested on measured data from the seabed characterization experiment (SBCEX 2017 and 2022) conducted in the New England mud patch. Preliminary results show that categorizing seabeds by porosity is a simpler and more robust approach than the previous catalog based on acousto-elastic parameters. In addition to investigating the seabed catalog, this work highlights the impact of water sound speed variability on deep learning results. Because the sound speed profiles vary between the 2017 and 2022 SBCEX data, differences between the performance of models trained on these datasets show the significance of understanding the water sound speed in order to accurately predict seabed type.

Ship noise for deep learning seabed classification

Due to climate change, the amount of ice cover in polar regions has become more variable. Because ice cover impacts sound propagation, proper sonar performance depends on an understanding of the presence or absence of ice. This project investigates the possibility of using deep learning to make this distinction. In preparation for ocean testing, initial measurements were collected in a laboratory tank. The experiment used a linear chirp from 10-50 kHz recorded at variable source and receiver positions in the tank. Approximately 150 liters of pebble ice were added to the water tank, and sound was recorded before ice was added, while it was present, and after it melted. From each one-second signal, the spectrogram was calculated and labeled based on the presence of ice. This labeled data was used to train a convolutional neural network (CNN) for binary classification for the presence or absence of ice. The trained CNN was applied to data collected on different days with different ambient noise conditions. The most successful CNN reached 81% accuracy on the unseen data. These results indicate the potential for deep learning to identify ice coverage in naval applications.

Acoustic Binary Classification of Ice Cover Conditions Using Deep Learning

General Problem: Future Distributed Maritime Operations (DMO) will rely on large-scale, heterogeneous swarms of unmanned systems (UxS). A critical barrier is achieving resilient autonomy in Disconnected, Intermittent, and Limited (DIL) environments. Standard distributed learning paradigms—such as Federated Learning—fail in these tactical scenarios for two reasons. First, they assume a fixed, reliable communication graph, which collapses under jamming. Second, they cannot handle "functional heterogeneity," where diverse agents (e.g., USVs vs. UAVs) possess incompatible state spaces, making simple parameter averaging mathematically invalid.

Proposed Framework: Sheaf Hypergraph Neural Networks. We propose a mathematically rigorous framework for decentralized representation learning based on Cellular Sheaves over Hypergraphs. Unlike standard Graph Neural Networks (GNNs) using scalar weights, we model the multi-agent system as a sheaf—a topological structure explicitly modeling algebraic constraints between local agent data.

Sheaf Diffusion for Resilient Consensus: We formulate coordination as a diffusion process governed by the Sheaf Laplacian. This operator drives the system toward a "harmonic" state (global consensus) via local exchanges. Because the diffusion is topological, it is inherently robust to node dropouts and dynamic topology changes common in contested spectrums.

Learning Geometric Constraints: We introduce a Sheaf Hypergraph Neural Network (SHNN) where the "communication protocol" is learned. The network learns Restriction Maps—linear transport operators that translate data between heterogeneous agents. During training, the model automatically discovers how to align feature spaces (e.g., sonar vs. optical) to create a unified common operating picture without a central fusion engine.

High-Order Hypergraph Modeling: We utilize hypergraphs to capture multi-way interactions (e.g., triangulation tasks), ensuring the learning process respects high-order mission dependencies rather than reducing them to pairwise links.

Naval Applications: This framework offers foundational technology for NIWC/NRL priorities in trusted autonomy:

Swarm Robotics and Heterogeneous Teaming: For Distributed Autonomous Systems (DAS), this replaces heuristic "flocking" rules with topological guarantees. It enables functionally diverse swarms to maintain formation and allocate tasks even when 50% of communication links are severed.

Collaborative Spectrum Sensing (EW): Distributed agents collaboratively classify hostile RF emitters. The sheaf framework aggregates partial signal features into a global result without transmitting raw IQ data, significantly reducing bandwidth.

Multi-Modal Sensor Fusion (ISR): A decentralized training framework for joint target recognition. Learned restriction maps mathematically ensure that acoustic, thermal, and visual features are fused consistently across the fleet.

Sheaf-Theoretic Hypergraphs for Resilient Distributed Autonomy and Learning in Contested Environments

Division 584 is developing several applied artificial intelligence (AI) capabilities to support cyber security engineering, including the Policy Related Assessment of Controls using Artificial Intelligence (PRAC-AI) and the Controlled Unclassified Information Categorizer (CUICAT); as well as the Technology Transition Classifier (TTC), the Technical Library Chatbot (TCLB) and AI@FlankSpeed. PRAC-AI, a Naval Innovation Science and Engineering (NISE) in NIWC PAC, ingests Risk Management Framework (RMF) test result exports (TREs), uses Python and the Pandas library to filter inherited controls, retrieves authoritative configuration information from policy documents, and generates draft test results using generative artificial intelligence (GenAI) and retrieval-augmented generation (RAG). The Information System Security Engineer (ISSE) maintains responsibility for approval, but PRAC-AI significantly reduces writing and formatting effort and improves consistency across assessments. CUI-CAT employ fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to detect and categorize all 126 categories of CUI, whereas the TTC categorizes weekly highlights into one of nine NAVWAR Technology Transition categories. The TLCB uses a lightweight retrieval-augmented approach to allow personnel to interact with Naval Information Warfare Center (NIWC) Pacific publications. In parallel, the division is collaborating with NearlyHumanAI through a Small Business Innovation Research (SBIR) effort on AI@FlankSpeed, an Azure-hosted, FlankSpeed-attached platform that builds custom generative AI workflows and exposes application programming interfaces (APIs) for integration with FlankSpeed applications and other cloud-hosted services.

Enabling Consumption of AI/ML Use Cases in the IL5 Environment

Naval tasking requires an intense focus that can burden cognitive resources and may reduce warfighter efficiency and performance accuracy. Understanding cognitive workload (CW) through brain computer interface (BCI) technology may provide solutions to optimize performance of operational procedures. This project reflects an ongoing effort [1] to measure and manipulate cognition through standard psychological tasks to develop a real-time CW classification approach. Here, we report on classifier performance of CW using electroencephalogram (EEG) cognitive task datasets for human in the loop optimization of warfighter performance. We created software (modified from a public source [2]) to reproduce a MATBII [3] cognitive workload experiment, where users perform a multi-tasking exercise with varying cognitive difficulty. We administered an experimental design to collect low and high CW data from our research team. We generated relevant plots to analyze our in-house data against highly processed public datasets. We structured and processed EEG data according to methods that can be conducted for real-time applications, including procedures for noisy EEG channel interpolation. In our search for an optimal real-time model, we tested multiple CNNs with and without pretraining on externally sourced CW datasets [4], using leave-one-out cross-validation for classification accuracy. Following our simulated real-time approach during MATBII task performance, we were able to achieve a mean cross-subject accuracy of 65% (range: 54%-70%) for binary classification of high vs low CW on our small dataset. Notably, we can obtain higher accuracies (mean: 73%; range: 66%-85%) by introducing additional subject-specific training and are currently researching methods to implement this reliably in real-time systems. Altogether, these results directly inform development of real-time BCIs for Navy use, and specifically for use cases that continuously demand and fatigue cognitive resources.
[1] Onners, J., Ronquillo, N., Trammel, T., Houst, D., Sengphanith, R., Wymbs, N. (2025, February 25). Real Time Cognitive Engagement Classification using CNNs. [Poster presentation]. NAML 2025, San Diego, CA, United States. https://doi.org/10.48448/tpkd-xg20
[2] Cegarra, J., Valéry, B., Avril, E., Calmettes, C., & Navarro, J. (2020) OpenMATB: A Multi-Attribute Task Battery promoting task customization, software extensibility and experiment replicability. Behavior Research Methods, 52, 1980–1990. https://doi.org/10.3758/s13428-020-01364-w
[3] Comstock Jr J. R., Arnegard R. J. (1992). The multi-attribute task battery for human operator workload and strategic behavior research. Available at: https://ntrs.nasa.gov/citations/19920007912.Lastedaccessthe02/02/2024
[4] Hinss, M.F., Jahanpour, E.S., Somon, B. et al. Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications. Sci Data 10, 85 (2023). https://doi.org/10.1038/s41597-022-01898-y

Cognitive Workload Estimation of Noisy EEG using ML

Problem Context
Machine learning efforts often begin not with “big data,” but with small, imperfect data. Legacy systems store information in non-machine-readable formats, and institutional repositories suffer from poor maintenance and broken links. These challenges compound when integrating data sources with misaligned data definitions, increasing ML workflow complexity. This work demonstrates a practical framework for making small, messy datasets analytically usable through careful preprocessing, transformation, and model selection.

Technical Approach
This paper demonstrates techniques for integrating heterogeneous government datasets to address foundational issues: (1) geospatial crosswalk construction using representative-point projection rather than geometric centroids for correct tract-to-region assignment; (2) variance stabilization for bounded, right-skewed rate data using Box-Cox transformation; (3) multicollinearity reduction in compositional features using isometric and additive log-ratio transformations; (4) model evaluation via stratified 10-fold cross-validation to address sample-size constraints, comparing linear regression and classification approaches. Together, these transformations mitigate spatial misalignment, heteroscedasticity, and multicollinearity while retaining observations—avoiding the need to remove influential cases (e.g., via Cook’s distance) or drop collinear features.

Results
Linear regression models achieved poor explanatory power (R² = 0.47 ± 0.28) with high cross-validation variance, indicating that continuous rate prediction is unreliable under current data constraints. Reframing the problem as binary classification (high-risk vs. baseline regions using 75th-percentile thresholds) yielded 91 % ± 4 % accuracy with F1 = 0.74 ± 0.16, demonstrating stable performance despite class imbalance. SHAP analysis provided ranked feature importance, enabling interpretable model outputs suitable for government decision-making contexts.

Implications
A well-designed pipeline combining careful preprocessing with appropriate model selection can yield actionable insights despite challenging data conditions. Geospatial crosswalk construction, variance stabilization, and compositional feature handling enabled meaningful analysis where raw data integration would have failed. While regression could not produce reliable forecasts (R² = 0.47 ± 0.28), classification identified high-risk regions (91 % ± 4 % accuracy), supporting prioritization and resource-allocation decisions. These methods are relevant for domains facing similar data constraints. The dataset and crosswalk methodology are publicly available on Kaggle for reproducible validation and adaptation.

David Elgas
MS Data Science Candidate, Harvard University
BSAE, Aerospace Engineering, San Diego State University


Premium content

Next from NAML 2026

P2P-Trig: An Efficient 2-Point Trigonometric Solution for Camera Pose Using Known Camera Parameters

Similar lecture

NAUTICAL wants YOU to learn AI!