[2604.25949] FalconApp: Rapid iPhone Deployment of End-to-End Perception via Automatically Labeled Synthetic Data

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Computer Science > Robotics

arXiv:2604.25949 (cs)

[Submitted on 21 Apr 2026]

Title:FalconApp: Rapid iPhone Deployment of End-to-End Perception via Automatically Labeled Synthetic Data

Authors:Yan Miao, Will Shen, Sayan Mitra<br>View a PDF of the paper titled FalconApp: Rapid iPhone Deployment of End-to-End Perception via Automatically Labeled Synthetic Data, by Yan Miao and 2 other authors

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Abstract:Reliable perception for robotics depends on large-scale labeled data, yet real-world datasets rely on heavy manual annotation and are time-consuming to produce. We present FalconApp, an iPhone app with an end-to-end frontend-backend pipeline that turns a short handheld capture of a rigid object into a perception module for mask detection and 6-DoF pose estimation. Our core contribution is a rapid mobile deployment pipeline paired with a photorealistic auto-labeling workflow: from a user-captured video of an object, FalconApp reconstructs an editable GSplat asset, composites it with diverse photorealistic backgrounds, renders synthetic images with ground-truth masks and poses, trains the perception module, and deploys it back to the iPhone frontend. Experiments across five rigid objects with diverse geometry and appearance show that FalconApp produces usable perception models with about 20 minutes of synthetic-data generation and training per object on average, around 30 ms end-to-end on-device latency on iPhone, and better overall pose accuracy than a PnP baseline on 4 / 5 objects in both simulation and real-world evaluation.

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Robotics (cs.RO)

Cite as:<br>arXiv:2604.25949 [cs.RO]

(or<br>arXiv:2604.25949v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.25949

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arXiv-issued DOI via DataCite

Submission history<br>From: Yan Miao [view email]<br>[v1]<br>Tue, 21 Apr 2026 20:33:08 UTC (9,846 KB)

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