Dan Milea

dblp:188/3654 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%
Artificial intelligence
1 paper
Robot navigation and mapping · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › mobile robot navigation › outdoor navigation
urban navigation
0.312026
City of Light (COL): A City-Scale, Geo-Anchored Urban Simulator with High-Throughput Multi-Sensor Streams · AAAI 2026

Methods — techniques the papers use, named apart from their topics

unity · 2.0multi-sensor streaming · 2.0
YearPublicationVenuePosition
2026 City of Light (COL): A City-Scale, Geo-Anchored Urban Simulator with High-Throughput Multi-Sensor Streams
abstract
We present City Of Light, a Unity-based, city-scale 116 km² simulator of Paris for high-throughput embodied AI research. COL fuses open geographic information system sources into geo-anchored, per-tile meshes and provides a configurable, stochastic runtime with controllable traffic and pedestrians. Agents receive frame-synchronized multi-sensor observations (RGB, depth, normals, semantics) and execute step-synchronized actions to navigate the environment. To support high-rate vision pipelines, we introduce TURBO, a Unity-Python bridge that streams multi-camera observations and allows control at up to 1300 FPS, achieving higher throughput than ML-Agents in our benchmark. We also provide a Street View Digital Twin that aligns simulator viewpoints with corresponding real-world panoramas for frame-accurate visual comparison and quantitative matching. COL enables fast scripting, large-scale data collection, and reinforcement learning in geo-anchored urban settings.
Ilias Sarbout, Mehdi Ounissi, Théo Cazenave-Coupet, Dan Milea, Daniel Racoceanu
AAAI4