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GauU-Scene: A Scene Reconstruction Benchmark on Large Scale 3D Reconstruction Dataset Using Gaussian Splatting

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arxiv 2401.14032 v1 pith:ZNSFPPVR submitted 2024-01-25 cs.CV cs.AI

Butian Xiong , Zhuo Li , Zhen Li This is my paper · ORCID
classification cs.CVcs.AI
keywords datasetgaussianreconstructionsplattingu-sceneanalysisbenchmarkdata
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abstract

We introduce a novel large-scale scene reconstruction benchmark using the newly developed 3D representation approach, Gaussian Splatting, on our expansive U-Scene dataset. U-Scene encompasses over one and a half square kilometres, featuring a comprehensive RGB dataset coupled with LiDAR ground truth. For data acquisition, we employed the Matrix 300 drone equipped with the high-accuracy Zenmuse L1 LiDAR, enabling precise rooftop data collection. This dataset, offers a unique blend of urban and academic environments for advanced spatial analysis convers more than 1.5 km$^2$. Our evaluation of U-Scene with Gaussian Splatting includes a detailed analysis across various novel viewpoints. We also juxtapose these results with those derived from our accurate point cloud dataset, highlighting significant differences that underscore the importance of combine multi-modal information

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. City-Level 3D Surface Reconstruction with Viewpoint Orientation Partitioning and Scene Completion

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Viewpoint-orientation partitioning of cameras plus selective completion of sparse SfM points enables higher-quality large-scale surface meshes from 3DGS than spatial-block baselines.

  2. AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    AerialMetric is a new benchmark dataset and evaluation suite for adapting monocular metric depth estimation models to real-world UAV aerial views.

  3. UAVScenes: A Multi-Modal Dataset for UAVs

    cs.CV 2025-07 conditional novelty 6.0 of 10

    UAVScenes adds frame-wise image and LiDAR semantic labels, reconstructed 6-DoF poses, and 3D maps to 120k frames of the MARS-LVIG dataset, with six benchmark tasks.

  4. Reconstruction Using the Invisible: Intuition from NIR and Metadata for Enhanced 3D Gaussian Splatting

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    NIRSplat fuses NIR imagery and vegetation-index metadata with 3D Gaussian Splatting via cross-attention and positional encoding, outperforming 3DGS, CoR-GS, and InstantSplat on the new multimodal agriculture dataset NIRPlant.

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