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arxiv: 2603.28287 · v2 · submitted 2026-03-30 · 💻 cs.CV

Recognition: no theorem link

TerraSky3D: Multi-View Reconstructions of European Landmarks in 4K

Mattia D'Urso , Yuxi Hu , Christian Sormann , Mattia Rossi , Friedrich Fraundorfer
Authors on Pith no claims yet

Pith reviewed 2026-05-14 21:45 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D reconstructionmulti-view stereodatasetEuropean landmarks4K imagesdepth mapscamera posesaerial imagery
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The pith

TerraSky3D supplies 50,000 high-resolution images across 150 European landmark scenes with calibrated poses and depth maps.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper identifies a shortage of large, high-quality public datasets suitable for training modern 3D reconstruction systems. To address this gap the authors captured and curated TerraSky3D, a collection of 50,000 4K images organized into 150 scenes that combine ground-level, aerial, and mixed viewpoints of European landmarks. Each scene is supplied with accurate intrinsic and extrinsic calibration, camera poses, and depth maps. The dataset is positioned as a resource that can be used directly to train and benchmark multi-view reconstruction pipelines under more realistic and varied conditions than earlier collections.

Core claim

The authors present TerraSky3D, a dataset of 50,000 high-resolution 4K images divided into 150 ground, aerial, and mixed scenes focused on European landmarks, each accompanied by curated calibration data, camera poses, and depth maps, created specifically to support training and evaluation of advanced 3D reconstruction pipelines.

What carries the argument

TerraSky3D multi-view dataset, consisting of high-resolution images with associated geometric annotations that span ground, aerial, and combined capture altitudes.

Load-bearing premise

The collected images, poses, and depth maps maintain consistent quality, accuracy, and diversity across scenes.

What would settle it

An experiment in which state-of-the-art reconstruction networks trained on TerraSky3D show no measurable improvement in accuracy or completeness when tested on standard benchmarks compared with networks trained only on prior datasets.

Figures

Figures reproduced from arXiv: 2603.28287 by Christian Sormann, Friedrich Fraundorfer, Mattia D'Urso, Mattia Rossi, Yuxi Hu.

Figure 1
Figure 1. Figure 1: Example scene from TerraSky3D. Left: Sparse reconstruction of the Villalta Castle, Italy. Right: Representative images collected from aerial and ground perspectives, shown with their corresponding semantically filtered depth maps. Abstract Despite the growing need for data of more and more so￾phisticated 3D reconstruction pipelines, we can still observe a scarcity of suitable public datasets. Existing 3D d… view at source ↗
Figure 2
Figure 2. Figure 2 [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example Scene from TerraSky3D. Left: Sparse reconstruction of the Natural History Museum, Vienna, Austria. Right: The first row shows example images, and the second row shows the corresponding semantically filtered depth maps. a diverse array of European landmarks and urban envi￾ronments, including medieval castles, historical buildings, arches, statues, fountains, bridges, dams, piers, shrines, as well as… view at source ↗
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example Scene from TerraSky3D. Left: Sparse reconstruction of the Italian Charnel House, Kobarid, Slovenia. Right: Represen￾tative images collected from aerial and ground perspectives, shown with their corresponding semantically filtered depth maps. While the entire dataset can be utilized for training large￾scale models, we propose an official data split to ensure consistent evaluation. Specifically, the … view at source ↗
Figure 6
Figure 6. Figure 6: Example Scene from TerraSky3D. Left: Sparse reconstruction of Erto e Casso, Pordenone, Italy. Right: Representative images collected from aerial and ground perspectives, shown with their corresponding semantically filtered depth maps [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example Scene from TerraSky3D. Left: Sparse reconstruction of the Barcis Dam, Pordenone, Italy. Right: Representative images collected from aerial and ground perspectives, shown with their corresponding semantically filtered depth maps. Tab. 5 reports the percentage of valid pixels at cumula￾tive thresholds demonstrating that our data exhibits higher geometric consistency across all error thresholds. It is… view at source ↗
read the original abstract

Despite the growing need for data of more and more sophisticated 3D reconstruction pipelines, we can still observe a scarcity of suitable public datasets. Existing 3D datasets are either low resolution, limited to a small amount of scenes, based on images of varying quality because retrieved from the internet, or limited to specific capturing scenarios. Motivated by this lack of suitable 3D datasets, we captured TerraSky3D, a high-resolution large-scale 3D reconstruction dataset comprising 50,000 images divided into 150 ground, aerial, and mixed scenes. The dataset focuses on European landmarks and comes with curated calibration data, camera poses, and depth maps. TerraSky3D tries to answer the need for challenging dataset that can be used to train and evaluate 3D reconstruction-related pipelines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript presents TerraSky3D, a new high-resolution 3D reconstruction dataset comprising 50,000 images across 150 ground, aerial, and mixed scenes of European landmarks, supplied with curated calibration data, camera poses, and depth maps to address the scarcity of suitable public datasets for training and evaluating sophisticated 3D pipelines.

Significance. A validated dataset of this scale and multi-view diversity could meaningfully advance 3D reconstruction research by providing challenging, landmark-focused captures that exceed the resolution or consistency limits of existing benchmarks. However, without demonstrated accuracy or comparative utility, the significance remains potential rather than established.

major comments (3)
  1. [Abstract] Abstract: The central claim that the dataset is 'curated' and suitable for training/evaluation rests on the unverified assertion of high-quality calibration, poses, and depth maps, yet the text supplies no capture protocols, reprojection errors, pose RMSE against independent SfM, depth RMSE against LiDAR/stereo, or any quantitative validation metrics.
  2. [Dataset description] Dataset description section: No ablation or histogram is provided on scene diversity (texture, lighting, viewpoint variation) or image quality statistics, which directly undermines the claim that TerraSky3D improves upon existing datasets limited by low resolution or internet-sourced variability.
  3. [Evaluation] Evaluation or experiments section: The manuscript reports no baseline 3D reconstruction results (e.g., on COLMAP or learned methods) or comparisons against Tanks & Temples / ETH3D, leaving the asserted utility for pipelines unsupported by evidence.
minor comments (2)
  1. [Dataset] Add a table breaking down the 150 scenes by category (ground/aerial/mixed) with exact image counts per scene to improve transparency.
  2. [Capture protocol] Clarify the 4K resolution claim with explicit pixel dimensions and any downsampling applied during capture or processing.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript introducing the TerraSky3D dataset. We address each major comment below and will incorporate revisions to strengthen the description and validation of the data.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the dataset is 'curated' and suitable for training/evaluation rests on the unverified assertion of high-quality calibration, poses, and depth maps, yet the text supplies no capture protocols, reprojection errors, pose RMSE against independent SfM, depth RMSE against LiDAR/stereo, or any quantitative validation metrics.

    Authors: We acknowledge that the original abstract and text did not include these quantitative details. In the revised manuscript we will expand the methods section with capture protocols (camera models, acquisition setup, and synchronization) and report specific metrics including average reprojection error from bundle adjustment, pose RMSE from cross-validation against independent SfM runs on overlapping subsets, and depth consistency RMSE measured against multi-view stereo reconstructions. LiDAR ground truth was not collected during acquisition, so direct LiDAR RMSE is unavailable; we will instead emphasize the stereo-based validation. revision: yes

  2. Referee: [Dataset description] Dataset description section: No ablation or histogram is provided on scene diversity (texture, lighting, viewpoint variation) or image quality statistics, which directly undermines the claim that TerraSky3D improves upon existing datasets limited by low resolution or internet-sourced variability.

    Authors: We agree that explicit statistics would better substantiate the advantages over prior datasets. The revision will add a new subsection with histograms and summary tables quantifying scene diversity: texture complexity via gradient magnitude distributions, lighting variation across capture times and conditions, viewpoint coverage (altitude, azimuth, and elevation ranges), and image quality metrics such as average sharpness scores and resolution uniformity across the 50,000 images. revision: yes

  3. Referee: [Evaluation] Evaluation or experiments section: The manuscript reports no baseline 3D reconstruction results (e.g., on COLMAP or learned methods) or comparisons against Tanks & Temples / ETH3D, leaving the asserted utility for pipelines unsupported by evidence.

    Authors: The manuscript is structured as a dataset release paper rather than a methods benchmark. To directly address the concern, we will add a concise evaluation subsection demonstrating baseline usability: COLMAP reconstructions on a representative subset of scenes with reported completeness and accuracy metrics, plus a side-by-side comparison table highlighting TerraSky3D's higher resolution, landmark focus, and multi-view (ground/aerial) diversity relative to Tanks & Temples and ETH3D. revision: yes

Circularity Check

0 steps flagged

Dataset release paper exhibits no circularity

full rationale

The manuscript is a dataset release paper that describes the capture and curation of 50,000 images across 150 scenes, along with provided calibration data, camera poses, and depth maps. There are no derivations, equations, predictions, fitted parameters, or load-bearing claims that reduce by construction to the paper's own inputs. The central assertion rests solely on the existence of the collected data rather than any self-referential reasoning, self-citation chains, or ansatz smuggling. No steps qualify as circular under the enumerated patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a dataset introduction paper. No mathematical derivations, free parameters, axioms, or new postulated entities are involved in the central claim.

pith-pipeline@v0.9.0 · 5452 in / 1082 out tokens · 53420 ms · 2026-05-14T21:45:32.301739+00:00 · methodology

discussion (0)

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