ULTRA-360: Unconstrained Dataset for Large-scale Temporal 3D Reconstruction across Altitudes and Omnidirectional Views
Authors: Xijun Liu
Zhaoliang Zhang
Yuxiang Guo
Yifan Zhou
Rama Chellappa
Cheng Peng
Venue: ICLR 2026 Poster (2026)
Keywords: large-scale 3D reconstruction
feature matching
novel-view synthesis
Primary area: datasets and benchmarks

Abstract: Significant progress has been made in photo-realistic scene reconstruction over recent years. Various disparate efforts have enabled capabilities such as multi-appearance or large-scale reconstruction from images acquired by consumer-grade cameras. How far away are we from digitally replicating the real world in 4D? So far, there appears to be a lack of well-designed dataset that can evaluate the holistic progress on large-scale scene reconstruction. We introduce a collection of imagery on a campus, acquired at different seasons, times of day, from multiple elevations, views, and at scale. To estimate many camera poses over such a large area and across elevations, we apply a semi-automated calibration pipeline to eliminate visual ambiguities and avoid excessive matching, then visually verify all calibration results to ensure accuracy. Finally, we benchmark various algorithms for automatic calibration and dense reconstruction on our dataset, named ULTRA-360, and demonstrate numerous potential areas to improve upon, e.g., balancing sensitivity and specificity in feature matching, densification and floaters in dense reconstruction, multi-appearance overfitting, etc. We believe ULTRA-360 can serve as the benchmark that reflect realistic challenges in an end-to-end scene-reconstruction pipeline.

Published as a conference paper at ICLR 2026
ULTRA-360: UNCONSTRAINED DATASET FOR LARGE-SCALE TEMPORAL 3D RECONSTRUCTION ACROSS ALTITUDES AND OMNIDIRECTIONAL VIEWS
Xijun Liu'; Zhaoliang Zhang'; Yuxiang Guo'; Yifan Zhou², Rama Chellappa', Cheng Peng JJohns Hopkins University, Baltimore, MD, USA *Zhejiang University, Hangzhou, China UUniversity Virginia, Charlottesville, VA, USA (x1iu253, zzhan288, yguo87, rchellar 1a4 1a4)e juu.. jhu.edu, yifanzezju. edu. en, xuz 71277wivirgnna.ed
ABSTRACT
Significant progress has been made in photo-realistic scene reconstruction over recent years. Various disparate efforts have enabled capabilities such multi- appearance or large-scale reconstruction from images acquired by consumer-grade cameras. How far away are we from digitally replicating the real world in 4D? So far, there appears to be a lack of well-designed dataset that can evaluate the holistic progress on large-scale scene reconstruction. We introduce collection of imagery on a campus, acquired at different seasons, times of day, from multi- ple elevations, views, and at scale. To estimate many camera poses over such a large area and across elevations, we apply semi-automated calibration pipeline to eliminate visual ambiguities and avoid excessive matching, then visually verify all calibration results to ensure accuracy. Finally, we benchmark various algo- rithms for automatic calibration and dense reconstruction on our dataset, named ULTRA-360, and demonstrate numerous potential areas improve upon, e.g., balancing sensitivity and specificity in feature matching, densification and floaters in dense reconstruction, multi-appearance overfitting, We believe ULTRA- 360 can serve as the benchmark that reflect realistic challenges in an end-to-end reene-reconstruction pipeline.
Figure Sample images of one building, Building #10, in our imagery collected over multiple seasons, elevations, and multiple camera types to enable fully immersive 3D/4D reconstruction.
"Equal contribution. Xijun Liu: data acquisition, calibration pipeline development, and feed-forward reg- istration experiments and evaluation. Zhaoliang Zhang: reconstruction experiments and evaluation. Yuxiang Guo: registration experiments and evaluation.

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INTRODUCTION
Immersive digitalization of the 3D world is of great interests for Computer Vision and Graphics researchers, with many real-world applications in Robotics, AR/VR, Autonomous Driving, Urban Planning, etc. Tremendous progress has been made in recent years with neural rendering innova- tions. Methods such as Neural Radiance Field (NeRF) (Mildenhall et al. 2020 and 3D Gaussian Splatting (Kerbl et al. (023) have improved quality in photorealistic scene reconstruction and novel- view synthesis. Various follow-up works have further extended this capability at larger scale (Turki et al. 2022
Liu et al. 2024a] Tancik et al.. 2022b), to model sites across time Martin-Bruale et al. Chen et al. 2022, Yang et al. 2023 Kulhanek et al.] 2024b; Xu et al.) 2024c] /Zhang et al.
2021
2024). and in various challenging scenarios, such as sparsity, incorrect exposure, or on degraded im-
ages (Zhu et al. 2023 Tang et al. 2025 Peng & Chellappa 2023] Gao et al. 2024 Wu al. 2025) With rapid progress and a vast amount of visual data online, we are closer than ever to achieving immersive 3D, perhaps even 4D, digitalization of the world. However, the current advances in neural reconstruction are often measured with disparate benchmarks and in specific aspects; collections of data from the internet (Snavely et al.) 2006; Wallingford al.. 2024) are also difficult to be used for accurate evaluations, given the myriad of uncontrollable variables within the collection.
Pose and the Structure-from-Motiie dense reconstruction. Inverse rendering work typically assumes known camera poses, but how re- alistically can we assume accurate camera calibration, particularly for large scale scenes? Methods that address multi inconsistencies (Martin Bruulla et al. 2021 Chen et al. 2022 Yang et al., 2023 Kulhanek et al. 2024b Xu al., [024c] Zhang et al. (024) work well on small scale scenes with dense camera coverage. Are these methods adaptable to sparser coverage or in large scale scenes? While open source efforts (Tancik et al. 2023, Yu et al., (022) have attempted to accom- modate different methods and datasets, they have largely stalled as software complexity grows over time. How realistically can we reconstruct city-scale scenes with only 2D images, in an end-to-end manner? We summarize two limitations in the current benchmark datasets for calibration, dense reconstruction and Novel View Synthesis (NVS):
(SfM) point cloud from camera calibration
essential to
Lack of Scale in Camera Coverage. Current datasets are typically limited in two areas: the per- spective camera format and the limited camera coverage. Perspective cameras are ubiquitous and easy use; however, their limited Field-of-
(FoV) lead to partial observation of the scene. As such, reconstructions are only viewable in one direction and are undesirable for immersive ex- ploration. The distribution of cameras are also focused on one aspect of the scene, e.g. either on the ground (Tancik et al., 2022a Mculeman et al. (023) or in the air (Turki et al. 2022 Crandall al. 20IT). While acrial observations can recover large scale structures, ground observations con- tain much richer details. Additionally, the limited camera coverage makes NVS evaluation overly reliant on test cameras that are close to training cameras, and does not reveal issues such as obvious floaters /Warburg et al. 2023) in unconstrained exploration of the 3D asset.
Lack of Scale in Realism and Time. Synthetic data from unlimited perspectives and FoV can be generated from virtual engines (Li et al.] 2023 Xiangli et al.] 2022] Mittal et al. 2023). but such data lacks realism. Real images are full of inconsistencies that cannot be fully simulated, c.g., lighting, scasonality, weather, etc. So far, datasets that demonstrate these realistic scenarios (Snavely ct al. 2006 /abour et al.] (023) are small in scale and difficult to evaluate against. For example, Phototourism (Snavely al. (006) comprises of internet images collected at unknown time. As a result, methods (Martin- Brualla et al. 2021 Xu et al. 2024b] Kulhanek et al. 2024a Xu et al., 2024a) developed these datasets requires access to test-view images during evaluation to optimize appearance information. Various temporal concepts such as seasonality and structural modifications are neither fully transient nor based on only appearance changes.
We propose a dataset for Unconstrained Large-scale Temporal 3D Reconstruction across Altitudes, named ULTRA-360. ULTRA-360 is collected at campus and aims to reconstruct and visualize the entire campus in 4D, with hundreds of videos collected across the span of two years, where the video frames are calibrated with manual inspection and aligned to a consistent coordinate system. ULTRA-366 provides:
1. Immersive Ground Acquisition: Both perspective and 360 panorama images on the ground level are collected and calibrated to facilitate immersive 3D reconstruction.
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2. Multi-Elevation Acquisition: Both ground and aerial images from multiple elevations collected and calibrated to ensure full coverage of the buildings.
3. Multi-Seasonalityy Acquisition: Images are acquired across multiple seasons in a two year period, capturing the gradual changes over time.
4. Large-Scale Calibration:
Twenty academic buildings are collected across multiple eleva- tions, camera models, years, and are calibrated together.
We perform detailed evaluations of current State-of- The-AArrt- methods on ULTRA-360, both in fea- ture matching and dense reconstruction. The results reveal encouraging process and challenges to be addressed. For feature matching, recent innovations allow us to find correspondences across large distances. Despite such progress. SoTA feature matching methods (Lowe 2004 DeTone al.) 2018 Edstedt et al., 2024b) Sun et al., [021] Leroy et al., 2024, Sarlin et al. 2020) lie between the spectrum of insufficiett true positives between images with large baselines and significant false positives between images with visual ambiguities. Scene graph optimization techniques (Cai et al.)
2023 Xiangli et al., 2024 Arandjelovic et al.) 2018 Duisterhof et al. 2024) can ameliorate such process, but still requires various manual intervention.
Dense reconstruction from multiple elevations suffers from difficulties in sufficient densification and severe sky floaters, despite improvements to Level-of-Details Multi-appearance modeling is often entangled with view-direction bias treated as a per-image optimization. We make several modifications, including neural sky modeling and time-based appearance modeling, to tackle these issues, and expect future research to improve immersive reconstruction based on ULTRA-360.
2 RELATED WORK
Table I: A comparison of existing multi-view datasets highlighting key properties, including scale, diversity of appearances, FoV, and viewpoint variation.
Dataser
images
Scale
Real/Synthetic
Time
Camera Type
Elevation
Photetourism
Snavely al. 2006
150K
Sceno
Real
Uncontrolied Uncontrolled
Perspeetive Perspective
Grvund
MegnScenes
2M
Scene
Real
Ground
Ble
1K
Scene
Real-Syntheric
Single
Perspective
Ground
UrbanSeonc30 30
Tandall 1
2011
128K
Scenc
Real-Synthetic
Single
Perspcetive
Acrial
Quad 6K
Cradun
5.1K
Scene
Real
Single
Perspective
Actial
19
3,6K
Single
Perspeative
Acrial
OMMO
La olal 00:5
14.7K
Scane
Real
Day/Night
Perspective
Acrial
Block-NeKi
20226
2.8M
Real
Day/Night
360
Ground
KITTL-360
300K
Single
Ground
NuSecnes
Cesar 01
2000
14M
City
Real
Day/Nigh//Rainy
360
Ground
MatriCCity
519K
Synthetic
DiI. Westher/Lighingg
Perspective
Ground+Aerial
ULTRA-360
37.7 K
City
Real
Four Seasons, Day/Night
Perspective 360
Gronnd-Aerial
2.1 MULTI- DATASETS FOR DENSE RECONSTRUCTION
In scene reconstruction and NVS research, the widely used benchmark datasets often focus on single objects (Mildenhall et al. 2020 Knapitsch al. 2017 Barron et al., 2022) indoor scenes (Lin) jet al. 2018). These datasets are collected in control led environments with accurate camera estima- tion. Various datasets (Snavely et al./ 2006 Tung al., 2024) construct outdoor unbounded archi- tecture datasets with multi-view images from the internet. While these datasets include appearance diversity, the uncontrolled collection method leads lack of multi-view imagery on single con- sistent appearance. Consequently, algorithms al. 2023 Kulhanek et al., 2024b: Xu et al. [2024c] Zhang et al.
-aartin-Buualla et al.
2021
Chen et al. 2022 Yang 2024) tested on these datasets
require access to test-view images during evaluation to account for unique appearance variation.
Large-scale datasets, such as Quad 6K (Crandall et al. 2011). UrbanScene3D (Crandall al., 201 Mill-19 (Turki et al.] (022), and OMMO (Lu et al. (023), have been collected from an aerial plat- form. This limits the level of details in reconstructed models, if rendering or exploration from the
ground perspective is desired. Driving datasets like Block-NeRF (Tancik et al.) 2022b). KITTI- 360 (iao et al. 2023), and NuScenes (Caesar et al. 20200 focus on street-level imagery, leading to many unobserved regions such as the roof of the buildings. So far, no dataset has been proposed for a large-scale collection of imagery spanning multiple elevations. MatrixCity (Li et al.) 2023) contains both ground and aerial imagery, but is synthesized through game engines.
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g
Gard
Figure 2: Visualization of the large-scale camera calibration process.
2.2
CAMERA CALIBRATION AND DENSE RECONSTRUCTION ALGORITTMM
Recovering dense 3D geometry from 21) images has a long history of research. Broadly speaking, camera calibration is first performed based on SfM (Schönberger & Frahm 2c06,Schönnergerr al.
2016
Pan et al., 2024). which relies on reliable feature extractors (DeTone et al. 2018; Lowe 1999 Edstedt et al. 2024a 2025) and feature matchers (Sarlin al., 2020, Lindenberger et al.
2023b Sun et 2021 Edstedt et al., 2024b Leroy et al.) 2024) to find correspondences. StM then performs triangulation recover camera pose and sparse geometry. Such a calibration process can be computationally expensive get stuck in incorrect solutions due to visual ambiguities; various scene graph optimization techniques (Arandjelovic et al. 2018 Berton et al. 2023 Cai et al. 2023 Xiangli et al., 2024) have been introduced to remove unnecessarily ambiguous image pairs based on prior knowledge. Selecting the proper scene graph or feature matching algorithms are highly subjective and unpredictable; while datasets from the Image Matching Challenging (Bellavia et al. 2025) exist, they are not constructed to be also used for dense reconstruction evaluation.
Dense reconstruction, photorealistic NVS, has progressed significantly with the introduction of NeRF (Mildenhall et al. (020) and 3DGS (Kerbl et al. 2023). By optimizing an implicit or explicit radiance field multi-view images through differential rendering. these methods can achieve pho- torealistic rednering quality. Follow-up works has improved upon NeRF (Turki et al. 2022 Tancik
al.) 2022a; Mi & Xu 2023; Reiser al. [0233 Xiangli al., [022]] Meuleman et al. 2023) and
3DGS (Lin et al. 2024 Liu et al. 2024b Lu et al. 2024 Ren et al. (0024) in large scale reconstruc-
tion, c.g., by splitting the scene into multiple blocks for optimization, introducing Level-of-Detane Detail rendering, multi-appearance modeling, etc. Evaluation is done on test cameras, typically in-between training cameras; however, no quantitative evaluation has been done on more free-formed and real- istic novel views. As shown in Table [ ULTRA-360 provides rich variations in both appearances, rendering FoVs, and cameras ranging from the ground to the sky, providing an unique opportunities to understand the effects of view-dependent effects, floaters, and details.
3 ULTRA-360
As shown in Figure 2 ULTRA 360 captures real-world large-scale imagery with multi-clevation. panorama coverage. and providing a comprchensive testing ground for evaluating modern scene reconstruction and NVS algorithms. This dataset contains over 30k calibrated images on twenty academic halls within a campus, covering an area of approximately 140 acre and a time period of two years. ULTRA-360 covers variety of texture and material, e.g., grass, glass/windows, trees, rocks, etc., that are on the campus. the following section, we describe the data collection process and the semi-automated calibration pipeline construct ULTRA-360.
3.1 LARGE-SCALE DATA COLLECTION ACROSS TIME AND ELEVATION IN 360 DEGREES
Constructing a dataset for large scale, immersive 3D reconstruction over time laborious, time- consuming, and computationally intensive. While professional photogrammetry software and de- vices exist, they are not scalable and difficult to integrate with novel research. To enable collection at scale in coverage and time, elect to use variety of consumer-gne gradde devices and develop own processing pipeline. As shown in Table [ for each of the twenty buildings, we systematically collect both aerial and ground-level imagery across four seasons with different lighting conditions.
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Table 2: Summary of ULTRA-360, where multi-view sequences are collected at different time, appearances, elevations, and FoVs.
Device
# Videos
# Frames
Scason Summer, Fall
Appearance
FoV
Elevation
iPhone
19
7134
Sunny, Cloudy, Night
70
Om
Insta360)
31
23260
Spring, Winter
Sunny, Cloudy, Night
360
0m
DJI Mini
81
7334
Spring, Winter
Sunny, Cloudy, Night
82"
60, 100, 120m
For ground imagery, the data collection process involves walking around each building's perimeter with an iPhone or Insta360 camera to capture video sequences. We perform manual inspection on all extracted frames to remove low-quality images and ensure sufficient overlap. Partieularly, panorama frames are split into four perspective images, each with a 120" FoV. These four frames together cover the horizontal 360 FoV around the camera. We discard the bottom face, which has a static human operator, and the top face, which is mostly sky. Any image that contains Personally Identifiable Information (PII) c.g., faces or vehicle license plates, are blurred through automated algorithms (Wu et al. 2019).
For aerial imagery, we operate DJI drones that follow circular flight trajectory around the build- ing. Drone flights are planned to ensure uniform and complete coverage. Multiple elevations are collected at 60, 100, and 120m. We also keep the ascending video sequences as the drone moves from ground level to approximately 60m above ground on two sides of each building. These as- cending videos help improve calibration between ground and acrial imagery. From these videos, we sample individual frames, applying the same quality control measures for ground-level data.
3.2
SEMI-AUTOMATED CAMERA CALIBRATION FOR DOPPELGANGER MITIGATION
After video acquisition and frame extraction, we build semi-automated pipeline to obtain correct camera calibration for all images. Given the sheer size in the number of images and covered area, directly applying software, e.g., COLMAP (Schönberger & Frahm 2016), is both infeasible and will lead to inaccurate results. As shown in Figure 2] we use divide-and-e wwiid--and-coquer approach by 1. calibrating images within elevation, 2. merging images across multiple elevations based on manually verified cross-clevation set, and 3. merging images from different buildings into a single coordinate system.
Image Calibration within an Single Elevation. For camera calibration, a collection of images I are collected at different times. Based on these images, scene graphs Garl and Gaerial can be constructed from the ground and acrial images. Scene graphs G = (I. P) consist of I as nodes, and image pairs extracted if edge P exists; such
P (L4 I edges. Correspondences between (I, I,) correspondences are then used for triangulation in SfM. Le.,, G determines the visibility of I to other images. Various implementations can be used determine scene graph edges. Exhaustive scene graphs are generally more accurate, but can also lead to more false matches.
Visual ambiguity, often referred doppelgänger (Cai et al. 2023) matches, occur cameras that are far apart due to their similar patterns. These doppelgängers are particularly common for ground image collection of buildings. Aerial images suffer less from visual ambiguities, as they have more global view of the building. For gaerial, we simply use exhaustive matching. For Gard, we use a mixture of sequential and exhaustive scene graph constructions to avoid doppelgängers.
Specifically, we denote multi-appearance ground images as I, where x denotes the video sequence and denote the frame within the sequence. Image pairs P = (P U (PT. heteeen can be fully separated into pairs that are within sequence and between any two sequences {x,y). For Pr Piitii we use sequential matching, i.e. Pritiin = (ZT",, - < 10). which prevents far-away frames to match. Such a spatial constraint is harder to determine for y as different sequences may not follow the same path or pace. To this end, manually bucket frames into 8 frrm and Shack which denote frames that are looking at the front or backside of the building. P. can then be define as:
P between
= (II (I)T j E Syon U (II "i E Shucks E 5
(1)
We find this setup effectively eliminates cross-sequence doppelgangers, as visual ambiguity within the same side of the building can be controlled by spatial constraints of individual sequences. For
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panorama images, which split into four perspective frames, P betwen against iPhone frames only involve the building-fate facing side of the panorama image.
Cross-Elevation Calibration. To connect calibrations from different elevations, we perform an ad- ditional calibration on cross-clevation set. Specifically, we calibrate a panorama ground sequence with aerial sequence. Registering cameras across large baseline is challenging, due to a lack of sufficient correspondences. To assist cross-elevation calibration, we record two transitional drone sequences from ground air for each building. Similar ground images, transitional drone images can experience visual ambiguities at ground level. The two sequences are distributed on the front and backside of the building. We manually define the scene graph geross elev i.e.
peross -(Pen) U (Prrss) } (P U (PPitis) } U {Piena) } U {'Pesril) J.
where p denotes image pairs between two elevations (note that P. = PH). For ground images, we apply sequential matching similar the ground-only scenario previously, i.e., P
gad = Within We do match ground and aerial images directly, i.c., perd 6, few accurate matches can be found and removing these pairs accelerate the feature matching process. Both Pagnal and paerial are ground-transition exhaustive. Finally, ground transition and transition-transiine transition pairings can be defined as:
Peans {(I.T)
)i E Serd j E
Strans)
U ((I
Ihtmm) | E Spuck J E Shack
front
(3)
{ Iuamss ) E Steat j E Ston } U {(Ztmms Igmans )| E Steak j E Stuck
trans
We use both SP+SG Tone al.) 2018 Sarlin et al. 2020 and RoMa (Edstedt et al.
2024b)
to compute correspondences based on parssseyY and COLMAP (Schönberger & Frahm 2016) to
perform SfM. Finally, select the best results from different matchers.
3.3 COORDINATE ALIGNMENT
Since SfM systems estimate camera up to an arbitrary scale and orientation, we need to align mul- tiple coordinate systems together. Given the same 3D points in two coordinate systems, Procrustes Alignment (Gower 1975) finds the scale, rotation, and translation {8, r,t) () transformations between them:
arg min 8,T.t
Σ |1<Tp's + - -p5p1
(4)
where pa and D'y are 3D points in coordinate system and V. To better align the camera systems, we optimize based on both the camera center Piss E R and rotation R E 3x33 Specifically, represent camera rotation by backprojecting three points based camera center and rotation:
Pi.X
Pink + + * Ri.X piL = PiLY + S'RRY
(5)
rot
where xx 1|,0*11, and x the standard deviations of pia"; x is similarly defined. Based on pi.X and pi."' we update Eq. 4 as follows:
(s" r "trr,,)= arg min 8.r.f
Σ
[1s(r pos
+t) - + [|s(r Pot + 1h
(6)
We show that this significantly improves the rotation alignment accuracy in our appendix.
Single Building Alignment. For every building, we obtain (sgna> r "ga (gaa) from the ground-only coordinate system to the cross-elevation coordinate system. This is done by applying Eq. (6) on the panorama sequence, which are calibrated in both systems. Similarly, we find (Steriat Vaerial ( for aerial cameras based the shared acrial sequence. All images of a single building can then be transformed into a unified coordinate frame.
Campus-wide Alignment To put cameras from all buildings into the same system, we perform similar alignment process. To accomplish this, we first calibrate subset of aerial images from every building, captured during summer from an altitude of 60m. Based the shared aerial images, we use Eq. (6) to find the transformation of every building's 's individual coordinate system to the campus-wide aerial calibration.

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Ground-truth
LoFTR
RoMa
RoMa
SP+5G
(a) Building #10
(b) Building #24
(c) Building #28
(d) Building M48
DaD+ RoMa
Figure 3: Visualization of multi-elevation camera poses obtained from different matching methods. EXPERIMENTS
We examine SoTA camera calibration and dense reconstruction algorithms on ULTRA-360. Specif- ically, two challenges are explored camera calibration: 1. finding true positive matches between far-apart images, c.g., across elevation; 2. avoiding false positive matches between images that are not visible to each other but have similar patterns. Two challenges are explored in dense recon- struction: 1. cross-elevation NVS and 2. aulti-appearance NVS. Through experiments, observe progress in these four challenges and many areas for future research to improve upon.
Cross-Elevation Feature Matching. For each building in ULTRA-360, select a portion of the front side perspective ground images and aerial images acquired at 120m, without the transitional images that connect them. We test six popular feature matching algorithms: SIFT (Lowe 2004). SP+SG (DeTone et al. 20118 Sarlin et al. 2020),SP+LG (DeTone et al. 2018 Lindenberger et al. 2023a), LoFTR (Sun al. 2021), RoMa (Edstedt al. 2024b). and RoMa filtered by two feature extractors, SuperPoint (De' (Delooe et al. 2018) and DaD (Edstedt et al. 2023). Exhaustive matching used for all scenarios mentioned above. In addition, we test four contemporary feed-forward matching methods: VGGSIM (Wang et al.) 2024), VGGT (Wang et al.) 2025). MASt3R (Leroy al. 2024) and MASt3R-SFM (Duisterhof et al. 2025). We report AUC@ 10, computed from Relative Rotation Accuracy (RRA) and Relative Translation Accuracy (RTA). To isolate the cross- elevation challenge, AUC is computed only over ground- acrial pairs. For each ground-acrial pair, measure the angular errors in rotation and translation and take the AUC of the minimum of RRA and RTA over 1 10-degrees threshold, a common metric for calibration.
As shown in Table. and visualized in Fig. calibrating cross-elevation images is challenging. In general, no algorithms can correctly calibrate all scenarios correctly. Interestingly, RoMa (Edstedt) al. (024b)-based methods the only ones with the ability to find cross-elevation correspon- dences. This can be attributed to its DINOv2 (Oquab et al. 2023) foundation model backbone. Despite the high sensitivity, RoMa (Edstedt et al., 2024b) is prone false positives, as ground im- ages are often falsely matched to each other due to similar patterns on the building. To this end, we find that SP TDe'Tone al. 2018) or Dal) (Edstedt et 2025) can help filter these false positives.
However, they can still fail in Fig. ((c) and (e).
(a) Front Back Building #54
(b) SP+SG Exhaustive
(c) SP+SG netVLAD
(d) SP+SG with DP+
(e) DaD+RoMa with DP++
Figure 4: Visualization of calibration with various scene graph optimization methods given visual ambiguity (a). All but (d) lead to suboptimal calibration solutions.
Automated Scene Graph Optimization. Various methods have been proposed optimize the viewing scene graph to remove visually ambiguous pairs (Cai et al. 2023 Xiangli et al. (0224) and reduce excessive computation (Arandjelovic et al. 2018 Berton et al. 2023). These approaches are important for unconstrained calibration, where sensitive feature matchers necessary, and false positive matches pose significant challenges. To this end, we evaluate several methods on ULTRA- 360, particularly the ground panorama sequences.
As visualized in Fig. 4 exhaustive matching often leads to the worst results both in accuracy and computation due to visual ambiguities. NetVLAD (Arandjelovic al. 2018) reduces computation
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Table 3: Cross-Elevation camera poses obtained from different matching methods. Measured in AUC@ 10 (higher is better).
Method
Building #10
#24
#28
#34
#48
#49
#54
LoFTR
0
SP+RoMa
0.3738
0.6986
0
0.5966
RoMa
0.0854
0.0023
0.0036
0,5030
0
0.1388
SP+SG
0
0
0
0
0
SP+LG
0
SIFT
0
DaD+RoMa
0.6941
0,8000
0.7915
0,5465
0.7440
0.6380
VGGT
0.1384
0
0
0,0003
0
0
VGGSIM
0
MAS13
OOM
0
MASGR--SM
0
0
0
Table 4: Quantitative evaluation multi-clevation reconstruction. We split the training set into either gnlund-only (G), aerial-only (A), or ground-aerial combined (GA) imagery. The test views also separated into ground-only (G) and aerial-only (A) subsets. Due to different collection conditions, we only evaluate DSIM in cross- elesseeleation rendering.
MERF
Splattacto-W W
CnyGS V2
Scaffold-GS
Ocnnee-G6 GS
EVER
Train
PSNR
SSIM
PSNK
SSIM
NSIM
PSNR
DSIM
DSIM
21420
D0609
0.118
21.925
0.657
0.366
20.I2
0.655
0.168
21.551
0.658
0.122
21,360
0.667
0.109
21.971
0.641
0.146
A
G
0.588
0.639
0.522
0.595
0.608
0.619
21,569
21.140
0.175
27/451
0.779
0.015
29,440
0.860
0.016
28.997
0.810
0.009
30,286
0.878
0.006
29.950
0.871
0.005
26.397
0.720
0.023
G
0.847
0.714
.....
0.743
0.822
0.755
0.740
13,43 0.106 0.407
23.206
0.042
0.598
26.135
0.022
0.759
0.034
23,433
by cutting down unnecessary pairs, but cannot resolve doppelgangers. Doppelganger++ (Xiangli al. 2024) simplifies the scene graph and address doppelganger to some degree; however, sensitive matchers like RoMa (Edstedt et al.) (2024b) still finds enough false matches lead to deformed calibration, whereas SuperGlue (Sarlin et al. (020) is less sensitive but more specific, achieving the correct solution. summary, selecting appropriate scene graphs and feature matchers to obtain good calibration still requires manual inspection and expertise. For more complete metrics and visualizations regarding image registration, please refer to our appendix.
Large-scale Dense Reconstruction and NVS. We select ten buildings from ULTRA-360 to evaluate current progress in robust, large-scale 3D reconstruction. For each building, we split training data into three configurations: 1. ground images only, 2. aerial images only, 3. both ground and aerial images. For each configuration, we evaluate from held-out ground and aerial cameras separately.
For baselines, we choose six SoTA methods for evaluation: Splatfacto- W (Xu et al. 2024a) Block- MERF (Song et al., 2024), CityGaussian (Liu et al.] 2024b).. Scaffold-GS (Lu et al. 2024). Octree-GS (Ren al. 2024) and EVER (Mai et al.) [024). Multiple metrics
used evaluate
NVS performance: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) are used for low-level quality evaluation. Perceptual similarity metries DreamSim (Fu et al. 2023) (DSIM) are used to quantify semantic similarity. which helps in cases where pixel-wise groundtruth is not available due to e.g., changed lighting conditions.
As shown in Table 4. and visualized in Figures and 0 we observe that Octree GS (Ren et al. 2024)
performs the best out of all methods, particularly when training data contain multi-clevation images. This can be attributed to its Level-of-Detail implementation. Scaffold-GS (Luu et al.) 2024) achieves comparable fidelity through hierarchical Gaussian decomposition. All methods perform much worse given cross-clevation images for training compared to using singlo-clevation only. Interestingly, this may not be due to limited capacity. As shown in Table [S] cross-elevation reconstructions have significantly less Gaussians compared to single-elevation reconstructions, despite being strictly superset in its training data. This likely indicates densification algorithms experience challenges when Gaussians' positional gradients are pulled from different directions.
By rendering cross- elevation cameras, we can observe various artifacts from current methods. Sig- nificant floaters exist when ground-only reconstructions rendered from aerial perspectives. No-
8

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Published as a conference paper at ICLR 2026
GT
GT Zoomed
Block MeRl
Splatfacto W
ChtyG V2
Scaffold GS
Octroe
EVER
Train on G
23.499
21.805/:566
18.8800472
22.200.299
22.44/6400
20,81:491
Train GA
PSNR/SSIM
Figure 5: Visualization of ground image rendering from different reconstruction methods and two training configurations: ground-only images (G) and ground+aerial images (GA).
GT
Block MeRF
Splatfacto-W
Ciyys V2
Scaffold-GS
Octnor-G
EVER
NeRFacio
DSSM
0.365
0.433
1,784
0.461
0.507
0.37%
0.331
CT
BlockMeRI
Splatfactu-W W
CityS
Scaffold-GS
OctreeGS
Ever
Octree, Misked
Figure 6: Top: ground view renderings from aerial- image-only reconstructions. Bottom: aerial view renderings from ground-image-only reconstructions.
tably, Splatfacto-V (Xu et al. 2024a) achieves superior acrial rendering through its background modeling. As shown in Figure 6, also implement implicit neural network model sky in Octree-GS, which significantly reduce floaters. CityGS (Liu et al., (202aa b) performs best on ground-view reconstruction from aerial data via geospatial-aware Gaussian priors optimized for large-scale aerial image.
Reconstruction and Zero-Shot NVS. ULTRA-360 contains multi-view quences collected at different time. We use these sequences to evaluate multi-appearan recon- struction. Wild-GS (Xu al.) 2024b) and Gaussian -iild (Zhang et (024) are used baselines, both of which require test image for evaluation. Unlike previous datasets (Snavely et al., 2006), ULTRA-360 has access to multi-view groundtruth every appearance. This allows us evaluate the effect of per-training-mman vious approaches lead to severe entanglement between view direction and the general appearances. Specifically, if we apply embedding from training image that is the farthest away from the cur- rent test view, a significant drop in performance can be observed. The larger the performance drop suggests that the embeddings and networks learned to overfit the input images, instead of the general 3D appearance. By modifying the per-image embedding to time-based embedding, we can both remove the reliance test-images render time and achieve more 3D consistent appearance modeling. We provide more details and visualizations in our appendix.
Multi-Appearance
embeddings on test images. As shown in in Table find that pre-
Table 6: Quantitative evaluation multi-an multi-appearance recon struction and rendering based ULTRA-360.
Table 5: The average number of 3D Gaus- sians under different training configurations
Cansiaa-
Splatfacto-W
CityS
ESNK
SSLM
DSIM
PSNK
SSIM
DSIN
5501244
307325
3191058
535201
Test Image Embreding
28.133
0.364
0.015
25.536
0.767
0.003
A
630093
287026
$27991
20738
Nearest Train image Eabediing
28,003
0.863
0.014
26.567
0.257
(1.00)
309018
241488
2230053
Farthest Trin Image Embaldiing
22. 22.506
0.770
0.061
25.631
0.757
0.023
Time Embedding
27.973
0.960
26.277
0.762
0.021

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Published as a conference paper at ICLR 2026
5 DISCUSSION AND CONCLUSION
In this work, propose a dataset called ULTRA-360 for Unconstrained Large-scale Temporal 3D Reconstruction across Altitudes. ULTRA-360 contains 37.7k frames collected from hundreds of videos across the campus and includes academic buildings from multiple seasons, multiple eleva- tions, and multiple camera types. To this end, we ensure cameras from different elevations can find correspondences based on ground-to-aerial transitional images. We also eliminate false matches through manually defined scene graphs.
Popular feature matching and scene graph optimization algorithms are evaluated to measure how imagery from ULTRA-360 can be calibrated without assistance. Some methods demonstrate sig- finding difficult true positives, at the cost more false positives. While proper filtering based on keypoint extraction bration pipeline still fall into incorrect solutions due to visual ambiguities, even with scene graph optimization. This showcases the need for a potentially more global approach in addressing doppel- gangers rather than relying pair-wise prediction.
nificant improvement
lead to less false positives, current camera cali-
We also evaluate various dense reconstruction methods ULTRA-360. We find that current meth- ods, even those designed for large scale reconstruction, perform much worse given cross-elevation images for training compared to using single-elevation only. This likely indicates limitations in densification algorithms at scale. Multi-appearance reconstruction is also benchmarked. Several methods require access to test-time images to model uppearance. Based on ULTRA-360, find that these methods tend to generate embeddings that are heavily over-fitted to specific viewpoint, leading to suboptimal results other views of the same appearance.
ULTRA-360 provides many novel directions for research, including the study on out-of -isstiiiiitttoi NVS, campus-scale immersive 4D reconstruction, and potentially serving as test- test-grounds for eval- uating geometric plausibility for generative models. In the future, we will continue to expand on ULTRA-360 to include more buildings and temporal variations.
ACKNOWLEDGEMENT
This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), IARPA R&D Contract No. 140D0423C0076. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed implied, of the ODNI, IARPA, the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright anno- tation thereon. The authors also thank the support and feedbacks from Dr. Ravi Ramamoorthi, UCSD.
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APPENDIX / SUPPLEMENTAL MATERIAL A
A.1 RECONSTRUCTED CAMPUS VISUALIZATION
Camera Calibration
Dense Reconstruction
CASNA
Figure A: visualization of the reconstructed campus.
Figure A shows a visualization of the reconstructed campus based on our collected imagery over two years. The dataset is collected over multiple seasons, elevations, and multiple camera types to enable fully immersive 3D/4D reconstruction. All images have been calibrated into unified coordinate system through semi-automated process and manual verification.
A.2 CROSS-ELEVATIC FEATURE MATCHING
As shown in Figure we provide additional visualization of camera pose estimations for five buildings using six feature matching configurations, complementing the results shown in Figure 3 Overall, DaD+RoMa achieves higher accuracy, successfully estimating more camera poses with lower error. However, it fails register the ground-level images in BigureBBal a) and encounters false positive matches in Figure Bic), demonstrating the challenge of cross-elevation feature matching and underscoring the necessity of adopting the proposed single elevation calibration strategy.
We also visualize the absolute eπrоr of each estimated camera pose with respect to the ground truth after alignment Figure C Specifically, we sort these errors in ascending order; for images that fail to be calibrated, we assign large enror. DaD+RRMa is generally capable of estimating most camera poses except [ Although SIFT struggles to register the multi-elevation images simuitaneously. the successfully estimated poses tend to exhibit lower error, indicating higher con- fidence. We also observe that RoMa without any feature extractor leads to unstable results, which is reflected in the gradual increase in across its estimated poses. This correlates with the ob- servation that RoMa's 's raw correspondences contain both many true positives and false positives. In general, LoFTR and SP+SG performs similarly, compromising between sensitivity and specificity.
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(a) Building #30
Building
Building #49
Ground-truth
SP RoMa
RoMa
SP SG
SIFT
(d) Building #54
(o) Building #78
DaD+ DaD+RoMa
Figure B: Additional visualization multi-elevation camera positions obtained from different matching methods.
325
140
(a) Building #10
(b) Building #24
(c) Building #28
Iole
(d) Building #30
(c) Building #34
Building #48
2mage
(g) Building #49
(h) Building #54
Building #78
Figure C: Visualization of multi-elevation camera position error across nine buildings
A.3 AUTOMATED SCENE GRAPH OPTIMIZATION
We provide examples in Figure D to demonstrate the challenge in visual ambiguities. Many buildings look similar from different angles. Exhaustive matching, e.g., with SP+SG, often fails. Without any knowledge of acquisition time, netVLAD (Arandjelovic al. 2018) sometimes can
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help prune away unnecessarily pairs to achieve better reconstruction; however, it's also very unreli- able. Doppelganger++ (Xiangli et al. (024) does better at eliminating confusing pairs, but different feature matchers can still be prone to errors in different scenarios.
Frone
Front & Back Building #28
SP+SG Exhaustive
SP+SG netVLAD
SP+SG with +P++
DaD+RoMa with DP++
Satellite View
Repetition Building #34
SP+SG Exhaustive
SP+SG netVLAD
SP+SG with DP++
DaD+RoMa with DP++
Satellite View
Symmetry Building #44
SP+SG
SP4SG netVLAD
SP+SG with DP++
DaD+RoMa with DP++
Exhaustive
Satellite View
Figure D: Visualization of calibration with various scene graph optimization methods given visual ambiguity. A satellite view is provided to demonstrate the true locations of the images.
A.4 LARGE-SCALE DENSE RECONSTRUCTION AND NVS
Our dense reconstruction evaluation uses PSNR, SSIM, and DSIM as primary metries. As percep- tual similarity metric LPIPS (Zhang et al. 2018 is also included in Appendix Tables and for completeness.
Table A: LPIPS multi-clevation reconstruction.
Table B: LPIPS of aulti-appearance recon- struction.
Yran
Taa
Blac--MFRF
Splaifarn-W
Dly08 V!
Okroo-688
FV
GA
63313
0,899
0.802
0,845
0.:22
0.550
0.661
01312
0.533
0.571
0143.5
0.541
0,443
0.381
0.487
0.467
0.529
0.503
Embeilinn
WIG-GS
0.114
Camselan- Wild
A
0.175
0.188
0.173
0.182
0.123
0.299
Nearest Image Embedding
0.900
0.381
0913
0.91
0.86
0389
Forthest Image Embobling
0.195
0.008
Time Embedding
0.118
0,298
A.5 MUL.T-APPEARANCE RECONSTRUCTION AND ZERO-SHOT NVS
Figure E shows the rendering results using different embeddings in the multi multi-appearance experi- ment. The difference maps between the rendered and ground truth images also shown. It can be seen that the image rendered with the embedding farthest from the training view exhibits a signifi- cant overall appearance difference. These visual comparisons highlight key drawback of per-image embeddings that they are viw-ddependent and lack consistency across different views.
A.6 COORDINATE ALIGNMENT
Mip-NeRF 360
Fnn(u) 4
Er(u) 4
Procrustes Alignment + RANSAC
0.196
0.0144
0.179
0.0114
+ Rotation Points
0.156
0.0117
Table C: Improvements over Procrustes Alignment baseline average rotation error En and trans- lation error Fey. Incorporating rotation points further minimizes the overall error.
We test the alignment algorithm on the Mip-NeRF 360 (Barron et al. 2022) dataset. Specifically, calibrate sparse subset of the images, then attempt to align it to the groundtruth coordinate
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Wild-IS
Imagee Embedding
Nearest Train-ing Emmh
Fanthest Train-ing Emnh
Embeekiing
Dir Maps
UISIM
0JI0S
0.005
0.024
0.007
Imaae Embedding
Train-ing
Train- img
kimbedding
Dif Maps
DSSM
0.009
0.009
0.002
0.010
Figure E: The rendering result of Wild-GS and Gaussian-Wins on different appearance embeddings. Zoom-in images are shown the bottom left and right; better viewed when magnified.
system. Sparse calibration leads inaccuracy, and makes the alignment process more noisy. As shown in Tab. Table applying constraint on both the translation and rotation points indeed reduce the rotation error significantly.
A.7 ADDITIONAL. SCENE RECONSTRUCTION VISCALIZATION
Please refer to the videos for additional rendering of 3D structures of the campus buildings.
A.8 THE USE OF LARGE LANGUAGE MODELS (LLMS)
We do not use any large language models in this work when constructing our dataset nor when drafting the paper.
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Reviews:
[{"review_id": "aiaXH0p4q7", "reviewer": "Reviewer_S12u", "summary": "The paper introduces ULTRA-360, a campus-scale dataset and benchmark targeting unconstrained, temporal, and multi-elevation 3D/4D reconstruction. The data spans ~140 acres over ~2 years, covering 20 academic buildings with both ground (perspective & 360°) and aerial (60/100/120 m) imagery acquired using consumer devices (iPhone, Insta360, DJI Mini), yielding >30k calibrated images (later summarized as 37.7k frames) with PII blurred. The authors build a semi-automated calibration pipeline: (i) within-elevation calibration via carefully constrained scene graphs to mitigate “doppelgänger” ambiguities, (ii) cross-elevation registration using transitional drone sequences from ground to air, and (iii) campus-wide coordinate alignment. They then benchmark feature matching (SIFT, SP+SG, LoFTR, RoMa; plus scene-graph optimizers such as NetVLAD and Doppelgänger++) and large-scale neural rendering methods (Block-MERF, Splatfacto-W, CityGaussianV2, Scaffold-GS, Octree-GS, EVER). Key findings: RoMa is the only method reliably finding cross-elevation correspondences but is prone to false positives; Octree-GS tends to perform best with mixed elevations; cross-elevation training often hurts densification; time-based appearance embeddings reduce overfitting relative to per-image embeddings.", "questions": "1. Beyond visualizations, can you report quantitative pose errors (e.g., ATE/RE) against a reference (e.g., campus-wide aerial bundle) and per-building success rates for different scene-graph/matcher settings? This would help substantiate the semi-automated pipeline’s reliability. \n\n2. How sensitive is performance to the front/back bucketing and the sequential window size |i−j|≤10? Could a learned side-classifier (or GPS/compass when available) reduce manual curation? Please provide failure cases and statistics.  \n\n3. Since you disable ground↔aerial matching (P_grd^aerial = ∅) as unreliable, what happens if only ground+high-altitude imagery exist (no transitional videos)? A “no-transition” benchmark variant and results would be very informative. \n\n4. You already include SIFT, SP+SG, LoFTR, RoMa. Could you add/compare LightGlue/MASt3R-SfM and report robustness vs. false positives in ULTRA-360’s doppelgänger regimes? (You discuss scene-graph choices; numbers for these newer matchers would round this out.) \n\n5. Consider a floater index (e.g., depth variance or 3D occupancy regularity under aerial renders of ground-only models) and a geometric plausibility score for free-navigation views. Even simple proxies would give a quantitative handle on the artifacts you highlight. \n\n6. You note cross-elevation training reduces Gaussian counts and likely stresses densification. Can you isolate whether this is due to optimization schedules, regularization, or scene-graph/pose noise? An ablation on densification thresholds and background/sky modeling across methods would clarify attribution.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Overall, this is a timely dataset paper with a clear gap and careful engineering. I appreciate it in the following aspests: \n\n1.Clearly motivated and realistic benchmark for end-to-end large-scale reconstruction across elevations and seasons; combines perspective and 360° ground coverage with aerial views. \n\n2.Thoughtful calibration strategy addressing doppelgänger matches via side-aware sequential pairing and cross-elevation transitional sequences; sensible divide-and-conquer pipeline.   \n\n3. Provide strong, diverse baselines & analysis covering both matching/scene-graph choices and modern 3DGS/NeRF variants, with concrete takeaways (e.g., RoMa sensitivity vs. specificity; Octree-GS advantages with multi-elevation).  \n\n4. A temporal appearance study with a simple but insightful time-based embedding alternative that reduces view-direction overfitting compared to per-image embeddings.  \n\n5. Privacy consideration via automated PII blurring in the released frames.", "weaknesses": "Although the topic is timely, the core contribution is largely data collection plus a semi-automated calibration pipeline with several heuristic/manual steps, which limits both novelty and technical depth for a ICLR main-track paper. The work would read stronger in a benchmarks/datasets track or at a vision/graphics venue focused on 3D reconstruction. Reproducibility/logistics also feel under-specified (clear license, canonical train/val/test splits by building/elevation/season, and standardized compute budgets/hyperparameters for baselines), which makes it hard to ensure fair future comparisons.\n\nWhile realistic, ~37.7k frames over ~20 buildings is modest relative to recent city-scale datasets such as Mapillary Metropolis, StreetGaussians, DL3DV-10K, or 4D4NeRF; the paper offers little direct comparison or clear positioning against them. It also omits strong contemporary cross-view/cross-modality matchers (e.g., MASt3R/DUSt3R, LightGlue, VGGSfM/++), leaving the central “cross-elevation” claim under-tested. The authors should articulate where ULTRA-360 is uniquely diagnostic (e.g., specific failure modes it exposes) and define standardized tasks/splits (no-transition cross-elevation calibration; ground→aerial / aerial→ground NVS; seasonal generalization) with apples-to-apples baselines to justify its distinct value.\n\nHere are some minor weakness: \n\n1. Manual components remain central (manual bucketing of “front/back” sides; manual verification of cross-elevation sets), which could limit scalability/reproducibility and make results sensitive to human curation.  \n\n2. Calibration quality reporting is mostly qualitative; clearer quantitative pose/rotation accuracy and failure analysis (per building/elevation) would strengthen claims about robustness. (The paper notes remaining ambiguities even with scene-graph optimization.) \n3. Cross-elevation dependency on transitional sequences may understate the difficulty when such data are unavailable; direct ground↔aerial matching is intentionally disabled as infeasible. \n4. Evaluation scope: image-quality metrics (PSNR/SSIM/DSIM) are reported, but free-navigation artifacts (e.g., floaters) are not quantitatively assessed; geometric plausibility metrics are suggested as future work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "F99Sx54PvK", "reviewer": "Reviewer_o2rg", "summary": "The authors present ULTRA-360, a campus-scale dataset that contains 37.7 k images extracted from hundreds of videos recorded over two years. Data span four seasons, three elevations (ground, 60 m, 100–120 m) and two device classes (perspective and 360° cameras). A semi-automatic COLMAP-based calibration pipeline is proposed to register all views into a single coordinate frame, followed by an empirical survey of six recent NeRF / 3D-Gaussian-splatting variants on cross-elevation and multi-appearance novel-view synthesis. The work is positioned as a benchmark that reveals “realistic challenges” in end-to-end 4D scene reconstruction.", "questions": "1. What are the technical novelties in the paper?\n2. Conduct comparative experiments with MASt3R and DUSt3R.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1.\tScale & diversity: To date, this is the largest real-capture repository that combines ground-level 360° imagery, drone footage, dense time sampling, and seasonal variation for the same physical site.\n2.\tThorough calibration effort: The authors manually verified every building-level reconstruction and performed cross-elevation alignment.", "weaknesses": "1.\tNovelty is primarily dataset-oriented, with limited methodological innovation. While the paper introduces a valuable large-scale dataset, it does not propose new algorithms, loss functions, or theoretical frameworks.\n2.\tThe calibration pipeline is an engineering assembly of existing components (COLMAP, SuperPoint/SuperGlue, RoMa) plus manual clean-up; \n3.\tFeature-matching ablations omit contemporary SOTA baselines such as MASt3R[1], DUSt3R[2].\n4.\tThe observation that “cross-elevation training reduces Gaussians” is reported but not explained; no ablation of densification schedule, initialization, or gradient statistics is given.\n[1] Grounding Image Matching in 3D with MASt3R\n[2] DUSt3R: Geometric 3D Vision Made Easy", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "C1XTax0Bx2", "reviewer": "Reviewer_eWid", "summary": "To address the limitations of datasets used in training NeRF and 3DGS, e.g., limited in scale, camera perspective diversity, the authors introduce ULTRA-360, a novel and comprehensive dataset designed to benchmark the holistic progress in large-scale, immersive 4D scene reconstruction. In addition, they conducted extensive comparisons using state-of-the-art methods on ULTRA-360 across two main tasks: camera calibration and dense reconstruction, even in some challenging settings. The observations from those experiments point the way for future research directions.", "questions": "No more questions, see Weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation is strongly justified and clearly articulated. It effectively identifies specific, well-known gaps in existing datasets (lack of scale, elevation diversity, temporal realism) and connects these gaps to practical limitations in evaluating SOTA algorithms.\n2. The ULTRA-360 dataset and its associated calibration pipeline is well-executed. The dataset's scale, multi-elevation, multi-season, and multi-camera nature is impressive. The semi-automated calibration pipeline, with its strategies for doppelganger mitigation and robust coordinate alignment, is a substantial technical contribution that adds great value to the dataset's utility and credibility.\n3. The experimental section is comprehensive, evaluating a wide range of SOTA methods on clearly defined challenges (cross-elevation feature matching, scene graph optimization, large-scale dense reconstruction, multi-appearance reconstruction). The results are convincing and clearly demonstrate the shortcomings of current algorithms. The analysis is insightful, correctly attributing RoMa's performance to its DINOv2 backbone and identifying the overfitting problem in multi-appearance embeddings.", "weaknesses": "1. In experiments, some SOTA methods are not included, e.g., VGGT, Pi3.\n2. The data is sourced solely from a single university campus environment, which limits scene diversity and the validation of model generalizability. \n3. The heavy reliance on manual intervention (e.g., manually defining scene graphs) in the calibration pipeline contradicts the goal of fully automated and scalable data collection, potentially introducing subjectivity. \n4. Its embodiment of \"4D\" is primarily confined to slow seasonal and illumination changes, lacking fast dynamics or geometric structural evolution in the scene.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3Hue5VdzPm", "reviewer": "Reviewer_oFTk", "summary": "The paper construct ULTRA-360, a video benchmark of buildings captured by different devices. The data of a building is captured in various seasons width different appearance from different elevations. The paper also presents a method to calibrate such a large number of images. Several methods are used as baselines to test on the ULTRA-360 for reconstruction.", "questions": "Will these data be made publicly available? Will ground truth be available for buildings when the dataset is published?\n\nWhat benefit is there in using images of buildings under different lighting conditions for 3D reconstruction? I think the images are useful for 4D reconstruction. However, I cannot find any benefits of these images for 3D reconstruction, except that they are troublesome.\n\nI couldn't see obvious visual differences between the images in Figure E in the appendix.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper contributes a building images dataset, where every building is captured at different seasons, under various weather conditions, from different viewpoints. The data are curial for generating building appearance variations over time. These data are not rare.", "weaknesses": "Although the main advantage of such data is 4D evolution, the paper did not take 4D experiments. Only 3D reconstructions were evaluated.\n\nTime evolutions of many kinds of data are important, but the dataset is only comprised of buildings. More complicated data are welcome, such as trees, whose appearance and geometric shape both change over time.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)