Neural 3D Object Reconstruction with Small-Scale Unmanned Aerial Vehicles Article Swipe
Àlmos Veres-Vitàlyos
,
Genís Castillo Gómez-Raya
,
Filip Lemić
,
Daniel Johannes Bugelnig
,
Bernhard Rinner
,
Sergi Abadal
,
Xavier Costa‐Pérez
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2509.12458
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2509.12458
Small Unmanned Aerial Vehicles (UAVs) exhibit immense potential for navigating indoor and hard-to-reach areas, yet their significant constraints in payload and autonomy have largely prevented their use for complex tasks like high-quality 3-Dimensional (3D) reconstruction. To overcome this challenge, we introduce a novel system architecture that enables fully autonomous, high-fidelity 3D scanning of static objects using UAVs weighing under 100 grams. Our core innovation lies in a dual-reconstruction pipeline that creates a real-time feedback loop between data capture and flight control. A near-real-time (near-RT) process uses Structure from Motion (SfM) to generate an instantaneous pointcloud of the object. The system analyzes the model quality on the fly and dynamically adapts the UAV's trajectory to intelligently capture new images of poorly covered areas. This ensures comprehensive data acquisition. For the final, detailed output, a non-real-time (non-RT) pipeline employs a Neural Radiance Fields (NeRF)-based Neural 3D Reconstruction (N3DR) approach, fusing SfM-derived camera poses with precise Ultra Wide-Band (UWB) location data to achieve superior accuracy. We implemented and validated this architecture using Crazyflie 2.1 UAVs. Our experiments, conducted in both single- and multi-UAV configurations, conclusively show that dynamic trajectory adaptation consistently improves reconstruction quality over static flight paths. This work demonstrates a scalable and autonomous solution that unlocks the potential of miniaturized UAVs for fine-grained 3D reconstruction in constrained environments, a capability previously limited to much larger platforms.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2509.12458
- https://arxiv.org/pdf/2509.12458
- OA Status
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- OpenAlex ID
- https://openalex.org/W4415315911
All OpenAlex metadata
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https://openalex.org/W4415315911Canonical identifier for this work in OpenAlex
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https://doi.org/10.48550/arxiv.2509.12458Digital Object Identifier
- Title
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Neural 3D Object Reconstruction with Small-Scale Unmanned Aerial VehiclesWork title
- Type
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-09-15Full publication date if available
- Authors
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Àlmos Veres-Vitàlyos, Genís Castillo Gómez-Raya, Filip Lemić, Daniel Johannes Bugelnig, Bernhard Rinner, Sergi Abadal, Xavier Costa‐PérezList of authors in order
- Landing page
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https://arxiv.org/abs/2509.12458Publisher landing page
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https://arxiv.org/pdf/2509.12458Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2509.12458Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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