Master Thesis: Monocular XR Object Localization with World Models

Technology, Data & Digital · Software & Web Development · Software Engineering · Data Science

In short

This Master thesis project in Stockholm, Sweden, will investigate the use of world models like NVIDIA Cosmos to enhance object distance and motion estimation from monocular XR video, addressing the lack of dedicated depth sensors in future XR devices. The research involves building a capture pipeline, training a lightweight model, and evaluating its performance against existing methods.

Responsibilities

Build a Quest 3 RGB-video capture pipeline with server-side inference.
Keep the Cosmos backbone frozen and train a lightweight probe to estimate object distance and motion.
Evaluate the system in controlled scenes with walking people and moving objects, using measured ground truth and depth-estimation baselines.
Assess accuracy, computational requirements, latency, and uplink requirements.

Requirements

A working prototype.
A documented dataset and measurement setup.
Quantitative accuracy and latency results.
A thesis report with recommendations for camera-only XR perception and network-assisted spatial intelligence.

Benefits

Outstanding opportunity at Ericsson.
Chance to use skills and imagination to push boundaries.
Opportunity to build solutions never seen before.
Challenging work in a team of diverse innovators.
Focus on diversity and inclusion.

Skills

PythonMachine LearningComputer Vision

#XR#object localization#world models#monocular video#depth sensing#NVIDIA Cosmos#machine learning#computer vision#Stockholm#Sweden

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