Experimental Assessment of Neural 3D Reconstruction for Small UAV-based Applications
- URL: http://arxiv.org/abs/2506.19491v1
- Date: Tue, 24 Jun 2025 10:25:17 GMT
- Title: Experimental Assessment of Neural 3D Reconstruction for Small UAV-based Applications
- Authors: Genís Castillo Gómez-Raya, Álmos Veres-Vitályos, Filip Lemic, Pablo Royo, Mario Montagud, Sergi Fernández, Sergi Abadal, Xavier Costa-Pérez,
- Abstract summary: Miniaturization of Unmanned Aerial Vehicles (UAVs) has expanded their deployment potential to indoor and hard-to-reach areas.<n>This paper presents a novel approach to overcoming these limitations by integrating Neural 3D Reconstruction (N3DR) with small UAV systems.<n>Specifically, we design, implement, and evaluate an N3DR-based pipeline that leverages advanced models to improve the quality of 3D reconstructions.
- Score: 12.16893489020317
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The increasing miniaturization of Unmanned Aerial Vehicles (UAVs) has expanded their deployment potential to indoor and hard-to-reach areas. However, this trend introduces distinct challenges, particularly in terms of flight dynamics and power consumption, which limit the UAVs' autonomy and mission capabilities. This paper presents a novel approach to overcoming these limitations by integrating Neural 3D Reconstruction (N3DR) with small UAV systems for fine-grained 3-Dimensional (3D) digital reconstruction of small static objects. Specifically, we design, implement, and evaluate an N3DR-based pipeline that leverages advanced models, i.e., Instant-ngp, Nerfacto, and Splatfacto, to improve the quality of 3D reconstructions using images of the object captured by a fleet of small UAVs. We assess the performance of the considered models using various imagery and pointcloud metrics, comparing them against the baseline Structure from Motion (SfM) algorithm. The experimental results demonstrate that the N3DR-enhanced pipeline significantly improves reconstruction quality, making it feasible for small UAVs to support high-precision 3D mapping and anomaly detection in constrained environments. In more general terms, our results highlight the potential of N3DR in advancing the capabilities of miniaturized UAV systems.
Related papers
- Large-scale Photorealistic Outdoor 3D Scene Reconstruction from UAV Imagery Using Gaussian Splatting Techniques [0.734084539365505]
We present an end-to-end pipeline capable of converting drone-captured video streams into high-fidelity 3D reconstructions with minimal latency.<n>Our goal is to propose an efficient architecture that combines live video acquisition via RTMP streaming, synchronized sensor fusion, camera pose estimation, and 3DGS optimization.
arXiv Detail & Related papers (2026-02-23T20:40:26Z) - Neural 3D Object Reconstruction with Small-Scale Unmanned Aerial Vehicles [16.745245388756533]
Small Unmanned Aerial Vehicles (UAVs) exhibit immense potential for navigating indoor and hard-to-reach areas.<n>We introduce a novel system architecture that enables fully autonomous, high-fidelity 3D scanning of static objects using UAVs weighing under 100 grams.
arXiv Detail & Related papers (2025-09-15T21:08:32Z) - IDU: Incremental Dynamic Update of Existing 3D Virtual Environments with New Imagery Data [9.026828976817992]
We introduce the Incremental Dynamic Update (IDU) pipeline, which efficiently updates existing 3D reconstructions.<n>Our approach starts with camera pose estimation to align new images with the existing 3D model, followed by change detection to pinpoint modifications in the scene.<n>A 3D generative AI model is then used to create high-quality 3D assets of the new elements, which are seamlessly integrated into the existing 3D model.
arXiv Detail & Related papers (2025-08-25T01:00:35Z) - UAVTwin: Neural Digital Twins for UAVs using Gaussian Splatting [57.63613048492219]
We present UAVTwin, a method for creating digital twins from real-world environments and facilitating data augmentation for training downstream models embedded in unmanned aerial vehicles (UAVs)<n>This is achieved by integrating 3D Gaussian Splatting (3DGS) for reconstructing backgrounds along with controllable synthetic human models that display diverse appearances and actions in multiple poses.
arXiv Detail & Related papers (2025-04-02T22:17:30Z) - Bridging Domain Gap for Flight-Ready Spaceborne Vision [4.14360329494344]
This work presents Spacecraft Pose Network v3 (SPNv3), a Neural Network (NN) for monocular pose estimation of a known, non-cooperative target spacecraft.
SPNv3 is designed and trained to be computationally efficient while providing robustness to spaceborne images that have not been observed during offline training and validation on the ground.
Experiments demonstrate that the final SPNv3 can achieve state-of-the-art pose accuracy on hardware-in-the-loop images from a robotic testbed while having trained exclusively on computer-generated synthetic images.
arXiv Detail & Related papers (2024-09-18T02:56:50Z) - Accurate Cross-modal Reconstruction of Vehicle Target from Sparse-aspect Multi-baseline SAR data [5.757535707973869]
Multi-aspect multi-baseline SAR 3D imaging is a critical remote sensing technique, promising in urban mapping and monitoring.<n>In the past, compressive sensing (CS) was the mainstream approach for sparse 3D SAR reconstruction.<n>Deep learning (DL) has emerged as a powerful alternative, markedly boosting reconstruction quality and efficiency.
arXiv Detail & Related papers (2024-06-06T15:18:59Z) - Optimized Deployment of Deep Neural Networks for Visual Pose Estimation
on Nano-drones [9.806742394395322]
Miniaturized unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring.
This work proposes a new automatic optimization pipeline for visual pose estimation tasks using Deep Neural Networks (DNNs)
Our results improve the state-of-the-art reducing inference latency by up to 3.22x at iso-error.
arXiv Detail & Related papers (2024-02-23T11:35:57Z) - FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language Models [59.13757801286343]
Few-shot class-incremental learning aims to mitigate the catastrophic forgetting issue when a model is incrementally trained on limited data.<n>We introduce the FILP-3D framework with two novel components: the Redundant Feature Eliminator (RFE) for feature space misalignment and the Spatial Noise Compensator (SNC) for significant noise.
arXiv Detail & Related papers (2023-12-28T14:52:07Z) - Instance-aware Multi-Camera 3D Object Detection with Structural Priors
Mining and Self-Boosting Learning [93.71280187657831]
Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field.
We propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector.
arXiv Detail & Related papers (2023-12-13T09:24:42Z) - Clean-NeRF: Reformulating NeRF to account for View-Dependent
Observations [67.54358911994967]
This paper proposes Clean-NeRF for accurate 3D reconstruction and novel view rendering in complex scenes.
Clean-NeRF can be implemented as a plug-in that can immediately benefit existing NeRF-based methods without additional input.
arXiv Detail & Related papers (2023-03-26T12:24:31Z) - NeRF-GAN Distillation for Efficient 3D-Aware Generation with
Convolutions [97.27105725738016]
integration of Neural Radiance Fields (NeRFs) and generative models, such as Generative Adversarial Networks (GANs) has transformed 3D-aware generation from single-view images.
We propose a simple and effective method, based on re-using the well-disentangled latent space of a pre-trained NeRF-GAN in a pose-conditioned convolutional network to directly generate 3D-consistent images corresponding to the underlying 3D representations.
arXiv Detail & Related papers (2023-03-22T18:59:48Z) - 3D Reconstruction of Non-cooperative Resident Space Objects using
Instant NGP-accelerated NeRF and D-NeRF [0.0]
This work adapts Instant NeRF and D-NeRF, variations of the neural radiance field (NeRF) algorithm to the problem of mapping RSOs in orbit.
The algorithms are evaluated for 3D reconstruction quality and hardware requirements using datasets of images of a spacecraft mock-up.
arXiv Detail & Related papers (2023-01-22T05:26:08Z) - HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for
Autonomous Driving [95.42203932627102]
3D human pose estimation is an emerging technology, which can enable the autonomous vehicle to perceive and understand the subtle and complex behaviors of pedestrians.
Our method efficiently makes use of these complementary signals, in a semi-supervised fashion and outperforms existing methods with a large margin.
Specifically, we embed LiDAR points into pixel-aligned multi-modal features, which we pass through a sequence of Transformer refinement stages.
arXiv Detail & Related papers (2022-12-15T11:15:14Z) - 3D Reconstruction of Multiple Objects by mmWave Radar on UAV [15.47494720280318]
We explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space.
The UAV hovers at various locations in the space, and its onboard radar senor collects raw radar data via scanning the space with Synthetic Aperture Radar (SAR) operation.
The radar data is sent to a deep neural network model, which outputs the point cloud reconstruction of the multiple objects in the space.
arXiv Detail & Related papers (2022-11-03T21:23:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.