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In-person
Americas
CV Meetups
Ann Arbor AI, ML and Computer Vision Meetup - December 9, 2025
Dec 9, 2025
5:30 - 8:30 PM
Cahoots Coworking Space 206 E Huron St, Ann Arbor, MI 48104
Speakers
About this event
Join our in-person meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.
Schedule
Challenges and Opportunities for AI in Marine Robotics
Underwater robots are essential tools for ocean exploration. This presentation will explore new technology in robotics and artificial intelligence that can enable automated detection of shipwreck sites from sonar data collected from autonomous underwater vehicles (AUVs). We will also discuss our recent work on underwater mapping and 3D reconstruction towards real-time dense mapping of shipwreck sites from sonar and camera imagery. Results will be demonstrated on data from field trials in Thunder Bay National Marine Sanctuary in Lake Huron.
From Raw Sensor Data to Reliable Datasets: Physical AI in Practice
Modern mobility systems rely on massive, high-quality multimodal datasets — yet real-world data is messy. Misaligned sensors, inconsistent metadata, and uneven scenario coverage can slow development and lead to costly model failures. The Physical AI Workbench, built in collaboration between Voxel51 and NVIDIA, provides an automated and scalable pipeline for auditing, reconstructing, and enriching autonomous driving datasets.

In this talk, we’ll show how FiftyOne serves as the central interface for inspecting and validating sensor alignment, scene structure, and scenario diversity, while NVIDIA Neural Reconstruction (NuRec) enables physics-aware reconstruction directly from real-world captures. We’ll highlight how these capabilities support automated dataset quality checks, reduce manual review overhead, and streamline the creation of richer datasets for model training and evaluation.

Attendees will gain insight into how Physical AI workflows help mobility teams scale, improve dataset reliability, and accelerate iteration from data capture to model deployment — without rewriting their infrastructure.