Pith. sign in

REVIEW 8 cited by

GauU-Scene V2: Assessing the Reliability of Image-Based Metrics with Expansive Lidar Image Dataset Using 3DGS and NeRF

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2404.04880 v2 pith:FCLYWIBR submitted 2024-04-07 cs.CV

Butian Xiong , Nanjun Zheng , Junhua Liu , Zhen Li This is my paper
classification cs.CV
keywords datasetlidarmetricsgaussiangauu-sceneimageimage-basednerf
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We introduce a novel, multimodal large-scale scene reconstruction benchmark that utilizes newly developed 3D representation approaches: Gaussian Splatting and Neural Radiance Fields (NeRF). Our expansive U-Scene dataset surpasses any previously existing real large-scale outdoor LiDAR and image dataset in both area and point count. GauU-Scene encompasses over 6.5 square kilometers and features a comprehensive RGB dataset coupled with LiDAR ground truth. Additionally, we are the first to propose a LiDAR and image alignment method for a drone-based dataset. Our assessment of GauU-Scene includes a detailed analysis across various novel viewpoints, employing image-based metrics such as SSIM, LPIPS, and PSNR on NeRF and Gaussian Splatting based methods. This analysis reveals contradictory results when applying geometric-based metrics like Chamfer distance. The experimental results on our multimodal dataset highlight the unreliability of current image-based metrics and reveal significant drawbacks in geometric reconstruction using the current Gaussian Splatting-based method, further illustrating the necessity of our dataset for assessing geometry reconstruction tasks. We also provide detailed supplementary information on data collection protocols and make the dataset available on the following anonymous project page

Discussion (0). Sign in to comment.

Forward citations

Cited by 8 Pith papers

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

  1. ProDiG: Progressive Diffusion-Guided Gaussian Splatting for Aerial to Ground Reconstruction

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    ProDiG progressively transforms aerial Gaussian splats into coherent ground-level 3D reconstructions via diffusion guidance and specialized attention modules.

  2. Beyond a Single Light: A Large-Scale Aerial Dataset for Urban Scene Reconstruction Under Varying Illumination

    cs.CV 2025-12 conditional novelty 7.0 of 10

    SkyLume contributes 10 real-world UAV urban regions captured at morning, noon, and afternoon with LiDAR-based ground truth, plus the Temporal Consistency Coefficient metric for cross-time albedo stability.

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

    cs.CV 2026-06 conditional novelty 6.0 of 10

    A 68K-pair aerial UAV depth benchmark shows standard monocular metric-depth models collapse in the air, and LoRA fine-tuning on it (MoGe2-Aerial) restores accurate metric depth on both aerial and ground scenes.

  4. 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.

  5. Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    An end-to-end neural transceiver jointly trained with 3D Gaussian Splatting loss enables sparse pilots and higher-fidelity large-scale 3D reconstructions from low-altitude aerial images.

  6. LSGS-Loc: Towards Robust 3DGS-Based Visual Localization for Large-Scale UAV Scenarios

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    LSGS-Loc delivers state-of-the-art accuracy and robustness for 3DGS-based visual localization in large UAV scenes via scale-aware initialization and reliability masking without scene-specific training.

  7. MetroGS: Efficient and Stable Reconstruction of Geometrically Accurate High-Fidelity Large-Scale Scenes

    cs.CV 2025-11 unverdicted novelty 5.0 of 10

    MetroGS combines distributed 2D Gaussian Splatting with structured dense enhancement, progressive hybrid optimization, and depth-guided appearance modeling to deliver higher geometric accuracy and stability in large-s...

  8. Proxy-GS: Unified Occlusion Priors for Training and Inference in Structured 3D Gaussian Splatting

    cs.CV 2025-09 conditional novelty 5.0 of 10

    A proxy mesh rendered through hardware rasterization provides a cheap occlusion depth prior that culls hidden anchors at inference and guides densification at training, giving Octree-GS-like MLP splatting a 3 to 4x sp...

Pith tools