CylinderSplat: 3D Gaussian Splatting with Cylindrical Triplanes for Panoramic Novel View Synthesis
Authors: Qiwei Wang
Xianghui Ze
Jingyi Yu
Yujiao Shi
Venue: ICLR 2026 Poster (2026)
Keywords: 3D Gaussian Splatting;Panoramic Novel View Synthesis;Cylindrical Triplane;Feed-forward;Multi-view Reconstruction
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Feed-forward 3D Gaussian Splatting (3DGS) has shown great promise for real-time novel view synthesis, but its application to panoramic imagery remains challenging. Existing methods often rely on multi-view cost volumes for geometric refinement, which struggle to resolve occlusions in sparse-view scenarios. Furthermore, standard volumetric representations like Cartesian Triplanes are poor in capturing the inherent geometry of $360^\circ$ scenes, leading to distortion and aliasing.

In this work, we introduce CylinderSplat, a feed-forward framework for panoramic 3DGS that addresses these limitations. The core of our method is a new {cylindrical Triplane} representation, which is better aligned with panoramic data and real-world structures adhering to the Manhattan-world assumption. We use a dual-branch architecture: a pixel-based branch reconstructs well-observed regions, while a volume-based branch leverages the cylindrical Triplane to complete occluded or sparsely-viewed areas. Our framework is designed to flexibly handle a variable number of input views, from single to multiple panoramas. Extensive experiments demonstrate that CylinderSplat achieves state-of-the-art results in both single-view and multi-view panoramic novel view synthesis, outperforming previous methods in both reconstruction quality and geometric accuracy.

Published as a conference paper at ICLR 2026
CYLINDERSPLAT: 3D GAUSSIAN SPLATTING WITH CYLINDRICAL TRIPLANES FOR PANORAMIC NOVEL VIEW SYNTHESIS
Qiwei Wang
Xianghui Ze²
Jingyi Yu!
Yujiao Shi!
Shanghaitech University
Nanjing University of Science and Technology
Inpul View
Input View I
Inpul View 2
Target View
Target View
Target View
Task I: Single View Synthesis
Task 2: Multi View Synthesis
Figure 1:
This paper introduces CylindorSplat, a foed-forward panoramic 3D Gaussian Splatting (3DGS)
framework for panoramic novel view synthesis from single (left) sparse (right) input views.
ABSTRACT
Feed-forward 3D Gaussian Splatting (3DGS) has shown great promise for real- time novel view synthesis, but its application to panoramic imagery remains chal- lenging. Existing methods often rely multi-view cost volumes for geometric refinement, which struggle to resolve occlusions in sparse-view scenarios. Fur- thermore, standard volumetric representations like Cartesian Triplanes are poor in capturing the inherent geometry of 360 scenes, leading to distortion and aliasing. In this work, we introduce CylinderSplat, a feed-forward framework for panoramic 3DGS that addresses these limitations. The core of our method is a new cylindrical Triplane representation, which is better aligned with panoramic data and real-world structures adhering to the Manhattan-worle assumption. We use a dual-branch architecture: pixel-based branch reconstructs well-observed regions, while a volume-based branch leverages the cylindrical Triplane to com- plete occluded sparsely-viewed areas. Our framework is designed to flexibly handle a variable number of input views. from single to multiple panoramas. Ex- tensive experiments demonstrate that CylinderSplat achieves state-of-the-ee sults in both single-view and multi-view panoramic novel view synthesis, outper- forming previous methods in both reconstruction quality and geometric accuracy. Our code is available at http://giiubbbcommwvggww///C/CW
INTRODUCTION
The proliferation of 360-degree cameras and the advancement of virtual reality (VR) technologies
have spurred significant interest in panoramic imaging. Panoramas offer a complete, immersive
field of view, making them an ideal medium for VR applications (Li et al. 2024a Yu et al. 2025 Luo et al. 2025) and a highly efficient data source for autonomous driving (Shi et al. 2019 2020
Zhu et al. 2021 A key challenge in this domain is novel view synthesis (NVS), which aims to
render photorealistic images from arbitrary viewpoints, providing users with a truly immersive and interactive experience.

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Published as a conference paper at ICLR 2026
ZX
ZX
Pine
YZ Panc
7X Plane
Plane
BR Plan
R4 Plaze
Carizson Triplns
(b) Spharaal Trpiin
OZ Plane
28 Piaze (d) Cylindrical Tripkaee
RB Plane
Camessin Volume
Spberical Volume
Cytindrica Volune
(4) Volume Differ ent Coorditae Systems
Figure 2:
Visualization of the Triplanc representation in (a) Cartesian, (b) Spherical, and (c) Cylindrical coordinate systems. (d) The corresponding unit volume elements for each system.
Recently, 3D Gaussian Splatting (3DGS) (Kerbl et al. 2023) has emerged as a breakthrough for real-time, high-fidelity novel view synthesis, with methods falling into two main paradigms. Optimization-based approaches (Kerbl et al. 2023 Lu et al.) 2024 Liu et al. [2024]] Huang et al.) 2024a) achieve exceptional quality via meticulous, per-seene optimization of millions of Gaussian parameters. However, this process is computationally intensive, requiring minutes to hours of train- ing per scene and offering no ability to generalize. In contrast, feed-forward methods (Charatan al. 2024 Chen al., 2024 Xu et al. 2025 Wei et al., 2025 leverage pre-trained deep
ral networks to predict the Gaussian parameters in a single forward pass, enabling near real-time reconstruction and generalization across scenes.
While 3DGS excels with pinhole cameras, adapting it to the unique geometry of panoramic imagery remains an active area of research. Recent efforts fall into two categories: per-scene optimization frameworks like (Huang et al. 2025a). and generalizable feed-forward methods such as (Zhang al.) 2025 Chen et al.) 2025). ). These feed-forward models typically predict an initial depth prior using depth foundation model, then refine it based on the cost-volume, but often fail in the presence of occluded regions, resulting inaccurate geometry and artifacts. Although alternative volumetric representations like Triplanes have been explored for refinement (e.g. (Wei et al. 2025 these solutions are tailored for pinhole cameras.
In this work, we introduce CylinderSplat, resolves the aforementioned limitations, as shown in Fig. | through dual-branch architecture.
new feed-forward framework for panoramic 3DGS that
Our first branch, the pixel branch, is inspired by recent advances in 3D reconstruction (Wang et al.) 2024 2025a Yang et al. 2025a). employs self-attention within frames and cross-attention among frames to aggregate multi-view information and produce feature point cloud. This design allows our network to flexibly handle an arbitrary number of input views and predict Gaussian pa- rameters corresponding to each input pixel. However, while the pixel branch generates high-quality Gaussians for well-observed regions, it fails in sperse-view scenarios where large baselines leave occluded areas without point cloud coverage. This results in significant holes and non-uniformity in the reconstructed scene, necessitating a dedicated mechanism for geometric completion.
To address this limitation, our second branch, the volume branch, which complements the pixel branch, introduces new cylindrical Triplane representation at each camera's 's location. This Triplane defines local volume, centered at the camera, that represents dense grid of points uniformly distributed in 360 cylindrical space. Its purpose is to correct geometric errors and hallucinate plausible details within the occluded regions of the pixel branch's 's output. To maintain efficiency, we use the Triplane to compress this dense 3D feature grid. Instead of storing features for all θ x 7 R points, the Triplane reduces the storage complexity from O( Z- R) to O(0 Z + Z R R @). This representation is initialized with features from the corresponding view's pixel branch and is further enhanced through triplane attention mechanisms.
The choice of a cylindrical coordinate system for our Triplane is at the core of our approach. Our inspiration comes from physics, where the choice of orthogonal curvilinear coordinates (such as spherical and cylindrical) (Fig. 2 and (c)) simplifies complex problems with symmetries. Our task is similar: we optimize 3D Gaussians distributed a 360 space around a central camera, so the spherical and cylindrical coordinate systems are natural comparison to the Cartesian sys-
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tems(Fig.)) [a)). On the other hand, most of the scenes in the real world adhere to the Manhattan- world assumption (Coughlan & Yuille 1999) that orthogonal surfaces dominate urban and indoor environments. A spherical Triplane (Fig, 2(b) struggles model these simple planes, but cylin- drical Triplane (Fig. (()) is exceptionally well-suited, as its Z R and RO planes naturally align with the vertical walls and horizontal floors prevalent in man-made environments.
The synergy between the flexible pixel branch and the geometrically aware volume branch, which constructs a local cylindrical Triplane for each input camera, enables CylinderSplat robustly han- dle a varying number of inputs, ranging from single to multiple. We provide extensive analysis in our experiments to validate the superiority of the cylindrical Triplane and demonstrate that our overall framework outperforms previous methods. Our primary contributions are summarized as follows:
A new cylindrical Triplane representation, compliant with the Manhattan-world assump- tion, designed to capture the unique geometric properties panoramic images.
A dual-branch ferd-forward framework, CylinderSplat, for panoramic 3DGS, combines pixel-based reconstruction for observed regions with volume-base completion for oc- cluded areas, enabling robust novel view synthesis from single multiple inputs.
State-of-the-arr performance in both quality and geometry accuracy for single- and multi- view panoramic novel view synthesis.
2 RELATED WORK
Novel View Synthesis for Pinhole Cameras. Novel view synthesis has rapidly progressed from NeRF methods (Mildenhall et al., 2021) real-time, high-fidelity 3D Gaussian Splatting
(3DGS) Kerbl et al., 2023). 3DGS methods fall into two main paradigms: computationally ex-
pensive per-scene optimization techniques that achieve exceptional quality (Kerbl et al., 20233 Lu
et al. 2024 Lin et al. 2024; Liu et al. 2024 Ww et al. (025), ) and generalizable feed-forward
approaches that use pre-trainee trained networks (Charatan al.] 2024 Chen et al., 20244) Wei al. 2025).
While recent feed-forward methods have scaled to large scenes (Huang & Mikolajezyk 2025 Cheng
al. 2025) Wang et al. 2025c] [liagg et al., 2025), all these 3DGS techniques are designed for pin-
hole cameras and fail handle the unique geometric distortions of panoramic imagery. Our work addresses this gap with a framework specifically engineered for panoramic data.
Novel View Synthesis for Panoramas. Novel view synthesis for 360 panoramas, challenging task crucial for immersive VR, has shifted from slow NeRF-based methods (Chen et al. 2023) to 3DGS. Current optimization-bart ct al. 2025a by Zhou et al., 2024] Pu et al. 2024) achieve high-quality direct panoramic rendering, but constrained by the cost of per-scene optimization. Conversely, faster feed-forward meth- ods (Zhang et al., 2025; [Chnn et al. 2025) are often limited to two input views, struggle with occlusions, and rely on inefficient indirect rendering pipelines. Our work bridges this gap by in- troducing feed-forward framework that adopts direct panoramic representation, utilizing new Triplane-based module to handle occlusions and refine geometry explicitly.
3DGS approaches (Yang et al., 2025b Li et al. 2025 Huang
Triplane Representations in Novel View Synthesis. Triplane- based representations have become a popular technique for encoding 3D scenes (Chan et al., [222 Shue al., [2023 Zou et al. 2024 Ju
& Li 2025 Xu et al. 2024) /Zhan et al. 2025). However, their application has been limited. Many
approaches are confined to small-scale objects (Chan al. 2022 Zou et al. 2024 Ju & Li 2025)
or rely on computationally expensive diffusion priors (Shue et al.
2023 Ju & Li 2025). While
some methods can handle large scenes (Xu et al. 2024 Zhan et al.
2025). they are restricted to per-
scene optimization. A notable exception is OmniScene (Wei et al. 2025), feed-forward method
for large-scale pinhole reconstruction. Despite its innovation, OmniScene's 's Cartesian Triplane is ill- suited for panoramic geometry, does not support multi-frame inputs, and can produce overly smooth renderings. To address this issue of blurriness, we introduce an RGB retrieval strategy that directly queries high-freyuency features from the input views, enhancing Triplane-hased renderings.
3 METHOD
We propose a dual-branch (pixel, volume) forward architecture for 3DGS reconstruction, opti- mized with three-stage curriculum. First, pixel branch (Sec. 3.1 is trained to establish high-
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Input View
Pixel Branch
Extractor A
Fentree Ganesan
MUP
Coumtan
Pixcl
Podi
Nekane
Ingut View 2
Final
Faaert Gauawian
Triplane Attention
Velume Gressian
Fenture
Volume
Feature Extractor S.
Triplane Attention
Festr Poins
Triptrr bv
Boad
Final
Cookr
ResNer
Seplass
feirm
Canorraa
GT
Figure 3: Overview of our CylinderSplat framework. Our method uses dual-branch architecture trained via three-stage curriculum. The pixel branch multi-view atlention mechanism generate high-quality Gaussians for well-observed regions. The volume branch designed to the gaps by lifting features into our cylindrical triplane representation, thereby completing the seene geometry robustly. The outputs from both branches are then unified for a final render.
quality baseline for well-observed regions. Next, with the pixel branch frozen, a volume branch (Sec. (.2) using
cylindrical Triplane is trained to provide robust geometric completion for sparse and occluded areas. Finally, both branches are jointly fine-tuned to merge the pixel branch's detail with the volume branch's completeness into single high-fidelity scene. The entire process is supervised by a composite loss function (Sec. 3.3 and our choice of the cylindrical Triplane is justified in the supplementary materials
3.1 PIXEL BRANCH
Prior forward methods (Zhang et al.) 2025 Chen al. 2025) typically follow a pipeline where they first use depth foundation model to predict a geometry prior, then refine this prior with a multi-view cost volume, and finally generate feature point cloud (Prea) from which to predict Gaussian parameters. However, this reliance on a cost-volume is computationally expensive and architecturally inflexible, as it requires a fixed number of input views and necessitates retraining to change the number of input views.
Our pixel branch is designed overcome these specific limitations. While also leverage initial depth prior from UniK3D (Piccinelli et al. (025), we replace the cost volume with a more efficient attention-basee mechanism, corresponding to our framework(Fig 3)s Feature Extractor, inspired by (Wang et al.) 2025a). We first use ResNet and a stack of T, = 6 attention layers to aggregate a rich, multi-view context. This network then predicts a refined depth map (Dpnn)), alongside feature map (L Using the refined depth, we unproject each pixel to its 3D coordinate to form Preat. where each point is endowed with the corresponding feature from F'aano Finally, these features are decoded into the complete set of Gaussian parameters, Gpiset- This camera-agnostic process is memory-efficient and provides a strong baseline for the volume branch to refine further.
3.2 VOLUME BRANCH
The volume branch is designed complete the geometry in under-observed and occluded regions by employing an efficient Triplane representation (Zou et al. 2024 Wei et al.] (025), which reduces memory complexity from ((V*) to O(V2). At the core of our approach the replacement of the standard axis-aligned Cartesian planes used in prior methods with new cylindrical Triplane that is better suited to the geometry of 360 panoramic scenes. For each input view's 's camera position, we initialize independent, local Triplane within cylindrical volume bounded by dimensions (Ru, Oo, Zo), as shown in Fig. a().. Each Triplane's three orthogonal feature planes (F,e. Faz, and F..) first initialized with set of learnable grid embeddings. Subsequently. illustrated in Fig. 3().
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populate these planes by aggregating the features of any points from the pixel branch that fall within the Triplane's 's volume. These view-specific Triplanes are then processed in parallel, each undergoing refinement via Cross-Plane and ciiplane-to-Imaeee Attention (Wei et al. 2025 Huang et a 2023 Li et al. 2024b) to decode a set of Gaussian parameters. Finally, the Gaussians generated from all individual Triplanes are concatenated to form the complete output of the Volume Branch.
3.2.1 CROSS-PLANE ATTENTION
Following initialization, the three feature planes(Fro, Faz. and F2r.) from the same cylinder must exchange information to form a cohesive 3D representation. We achieve this through our cross- plane attention mechanism, where each feature on one plane queries corresponding features from the other two planes along the orthogonal dimension. For example, consider updating feature fez (i.j] in the Fès plane, we sample N, points along the corresponding radial axis, indexed by k E (0,...; ,,, 1), and retrieve features from the other two planes(Fro and F2r) ) these locations to serve as the keys and values. An attention mechanism then computes a weighted sum these values based their similarity to the query. This update process, including residual connection, is executed in parallel for all features and can be conceptually expressed as:
N
f. =
for (i,5)
Σ k=0
W
(ggk) (j, + wi9 (ko
(zjk)
(1)
Here, f2 (4.) is the updated feature, and the coefficients w are the aggregation weights produced by the attention's 's softmax over the query-key scores. This comprehensive fusion ensures a unified spatial representation.
3.2.2 TRIPLANE-TO IMAGE ATTENTION
To enrich the Triplane features with visual evidence from source images, we perform an additional refinement step using Triplane-to-Image Attention. In this mechanism, the triplane features fused in the attention step of the cross-plane attention act as queries, while the panoramic image features F Fpono from the pixel branch serve the keys and values. For example, in the update process for to (i.j) in the F%, plane, we sample N2 points along the orthogonal radial dimension, indexed by {0..... No 1), and project each resulting 3D point (0,, 2y. into the panorama to obtain its corresponding pixel coordinates (uijk, Bizk). The characteristic of the panoramic image p(ijk) at this location then recovered from Fpano- This set of features aggregated into a single context vector via the cross-attention mechanism and added back to the original query through residual connection. This update rule can be conceptually summarized as follows:
NC-1
Eh (i.)) = f(. (1,7) + Σ
w ((ik)) pano paño
p(ijk)
Here, f (i,j) is the final visually enriched feature, and the learned attention weights Wpano ensure that the final 3D representation is closely aligned with the 2D input images.
3.2.3 DECODING GAUSSIANS FROM THE TRIPLANE
For each per-camera Triplane, we generate the corresponding volume Gaussian primitives by first uniformly sampling dense grid of N, x No x N₂ points within its cylindrical volume. At each grid point (0i, æj. fk), we compute its feature f,oz by querying and summing the corresponding features from the three refined tri-planes: f" (k, i) r (i, + f (j. k). ML.P (Rosenblatt, 1958) then processes this aggregated feature to predict set of local, normalized parameters for each potential Gaussian: {o Speal, R, a volume = ALPP(f.ez) Here, diocal = (8r, dø. dz) represents positional offsets and S = (S,, So, Sz) represents anisotropic scaling factors, both defined within the local cylindrical coordinate frame of the grid cell.
Since the projection coordinate system for 3DGS remains Cartesian, still need transform these locally defined cylindrical attributes into Cartesian coordinates. The final position x' = (z', // 2') is calculated by applying the learned offsets òroeal the base cylindrical coordinates of the grid
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cell 2)) 2) and then performing standard cylindrical-to-Carteee -aCattssin conversion:
(a' 2 2²) (-(r + sin( åg); 83, -(r + 8,) cos(0 ( 6c)).
Crucially, the local scaling factors Storal must also be transformed to represent the Gaussian shape in Cartesian space correctly. This is achieved using the Jacobian matrix J of the coordinate trans- formation. The final anisotropic scale S' is computed
S'
Stocal:
(4)
where [J] is the matrix of the absolute values of the Jacobian's elements, and J is defined as:
sin ( + 68)
-(r + 6r) cos(O 0g)
0
J
0
1
(5)
cos ( 50)
(r 5,) sin 60)
3.2.4 VOLUME RGB RETRIEVAL
Since the features derived from the Triplane are high-level and semantic, they often lack the high- frequency details necessary for photorealistic color. To address this, we determine the color C for each volume Gaussian an RGB Retrieval mechanism inspired by (Miao al. 2025), which leverages information directly from the source images.
For each Gaussian, we project its center x' into N, source views to retrieve the pixel colors {C, )- To handle occlusions, compute visibility score for each view, 8, dg do. where d, is the distance of the Gaussian from its corresponding camera and d, the reference depth from Unik3D at that pixel. A smaller score indicates higher visibilityy A final MLP then predicts the definitive color based on a risibility-weighee aggregation of these retrieved colors:
C = MLP Σ
w. Cp
where wa softmax(-su).
(6)
Here, C is the final predicted color and wy is the learned visibility weight. This visibility- aware approach encourages the model to learn color from the most reliable, unoceluded views, providing an implicit supervisory signal to align the Gaussian geometry with the depth priors. The full pa- rameter set for each Triplane-bast based Gaussian is thus given by {x', S', ", C, a volume- Finally, we concatenate the Gaussians from all per-camera Triplanes to form the complete output of our volume branch, Gvolume-
3.3 LOss FUNCTION
Our model is optimized using composite rendering loss, Crender applied consistently across our three-stage training curriculum. This loss enforces photometric accuracy, perceptual realism, and geometric consistency. It is weighted sum of three components:
/ + 0.05 CLPIP((7, [g)) + 0.1 || Des
frender =
where i and D are the rendered image and depth map, Ige is the ground-truth image, and D is the reference depth map from UniK3D. The source of the rendered outputs changes with each training stage: first using only pixel branch Gaussians (Spisel), then only volume branch Gaussians (Gvolume,, and finally the union of both (Gpixel U @volume) for joint fine-tuning.
4 EXPERIMENTS
Datasets. To ensure fair comparison with prior feed-forward panoramic methods, we follow the
experimental setup of (Zhang et al.) 20255] Chen et al. (023). We evaluate our model on three synthetic datasets- Matterport3D (Chang al. 2017), ), Replica (Straub et al.] (219). and Residen- tial (Habtegebrial et al.] 2022) and one world dataset, 360Loc (Huang et al. 2024b). All experiments conducted using images with resolution of 512 x 1024.

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Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison for the two-view reconstruction on the Matterpart3D Replica, and Residential datasots. The nist second and third besi results are highlighted. Meihouls marked with indicatee that we reimplereetted them using their official code.
MatepesD
Rusdan
2.5m
Velial
WSS. FINT
1 1996]
WCCT
WS PAKRT
18999)
KXT
1A PENRT
EPPS.
PCC
WS
FENC1
[990S]
WS PSORT
18994)
NVVSaa
15.31
0.955
0.524
21.32
0.97
1.300
28.19
0552
0.135
2054
0995
0.055
24.21
0.300
1.30
2006
0.76
0.322
22.36
8811
0.22
21:12
0276
0115
2022
1907
0114
31.83
[6792
35.79
0.00
0.75
23.35
6-3M
69%
55.03
0787
27:4
4.557
Zasipla
24.6
0.77
9.26
0.79
24.99
E319
28:3
6035
0681
0/61
2679
0U82
0.865
38.97
1.72
8281
25.76
0.175
0.90
25.91
8573
0.386
8525
2039
0037
0/81
0285
3420
1099
8,857
0.53
25
0.56
Table 2: Quantitarive comparison the single-view reconstruction task.
Malepe3D
2.mm
1.5
1.5m
Vidial
PCC-
WS FINE-
1999)
ICC1
WS
PARR
SUM
18995)
PENRY
LPPS,
PCCH
WS FENR1
PC01
WSS
PSART
SUM
18995)
Oredireet
22.58
0.30
0.24
11
0.36
5886
75.32
6196
01157
0/775
0.714
97
177
21:81
0.00
0:80
0.08
32.00
590
5:390
6551
15
0.788
26.66
0.055
0.51
STA
24.35
Pedppact
09.22
0,425
0334
0.57
21
896
6.322
B621
22:43
07177
0233
0/81
25.33
0.09
0114
Table 3: Quantitative comparison for the twa-
view reconstruction task on the 360 on dataset.
Datrect
3600as (ang. 1.40m lanclnng)
Method
POUT
WS-PSNKF
SS1M?
LPIPS
MV5
24.67
0.823
0,170
Tapst Viees
Target
OrraSoras Spirae PraSpar
OmniScene
0.830
27.41
0,153
Figure 4: Qualitative comparison of 3600 (two-view). Left: inpushurgee views. Right:
Splaterre0
0.866
26.14
0.860
0.32)
zoomed-in novel views and depth maps (warm far, near), Our ground truth (GT)
ParSplai
0.168
25.24
0.875
0.108
depth obtained from DepthAnywhere (Wang & Liu 2024 which serves as a reference for calculating PCC.
Ours
28.35
For the synthetic datasets, each represented by a freeefframe sequence, we train our model on 20,000 sequences from Matterport3D, where adjacent frames are separated by 0.5m. We then test on held-out sequences from Matterport3DD Replica, and Residential with varying baselines from 0.3m to 2.0m. For the two-view reconstruction task, consistent with prior work (Zhang et al. 2025 Chen et al. 2023), we use the first and last frames of each sequence as input (1.0m bascline) to reconstruct the middle frame as the target. To validate our model's flexibility, single-view reconstruction experiment by fine-tuning our two-view model. For this task, we use the middle frame of each sequence as the sole input to predict the two outer frames as rendering targets.
also conduct
For the real-world dataset, 360Loc (Huang et al., 2024b) includes four scenes captured in diverse campus-scale indoor and outdoor environments, comprising 18 sequences of continuous frames cap- tured at 0.46m intervals. We follow ęZhang et al. 2025)'s 's fine-tuning and evaluation setting. We first fine-tune our pre-trained two-view model (trained on the synthetic datasets) on three scenes (13 sequences) from 360Loc. Subsequently, we test it on the held-out scene (5 sequences). During both fine-tuning and testing, we use two input frames with a 1.4m separation (skipping two intermediate frames) and render all four views (the two inputs and the two intermediate frames) as output targets.
Evaluation Metries. Consistent with prior work (Chen et al. [023] Zhang et al., 2025), for image quality, we report SSIM (Hore& & Ziou 2010). LPIPS (Zhang et al. (0188), and WS-PSNR (Sun) jet al. (017), using WS-PSNR for its robustness to panoramic distortions. For geometry, we com- pute the PCC (Benesty et al. (2099) against reference depths generated by DepthAnywhere (Wang) & Liu 2024), using this scale-invariant metric to provide a measure in the absence of GT depth.
4.1 COMPARISONS WITH THE STATE-OF-TIEE-ART
We conduct a comparison against several state-of wwwoooffthhaaae an foed-forward methods, follow- ing the two-view reconstruction setup from Zhang et al. (025) for both synthetic and real-world
scenes. Our baselines include two panoramic 3DGS models, PanSplat (Zhang et al. 2025) and Splatter360 (Chen et al.] 2025), panoramic NeRF model, PanoGRF (Chen et al. 2023). ), and two methods adapted from the pinhole domain: the surrounding-viee and the multi-view MVSplat (Chen et al. 2024), as shown in Table [
OmniScene (Wei et al., 2025)
Figure 5 Figure 6 and Fig-
ure Results for MVSplat, PanoGRF, and PanSplat are taken from Zhang et al. (2025). while OmniScene and Splatter360 were reimplemented using their official code. Since OmniScene only supports pinhole cameras, we adapt them by decomposing each panoramic image via cubemap pro- jection. Additionally, we conduct a single-view reconstruction comparison the synthetic datasets
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GT
Input Views
Splatter360
PanSplat
OnniSeene
Ours
Matterport3D (2.0m baselinc)
Replica (1.0m baseline)
Residential (0.3m baseline)
Figure 5: Qualitative comparison of synthetic scenes for the two-view input task across different base- lines. The top two rows display the target ground truth and input views. followed by comparisons of differ- ent methods. Zoomed-in regions highlight the superior completeness, sharpness, and reduced artifacts of method.
View
OmniScene
Figure 6: Qualitative depth comparison on synthetic scenes for the two-view input task. From left to right: ground-truth RGB, the reference depth from DepthAnywhere (Wang & Liu 2024). and the depth maps from different methods. Our result shows the hest consistency with the reference, particularly on the floor and ceiling.
in Table 2 As the cost-volume mechanisms in PanSplat and Splatter360 require at least two views, create fair baseline by fine-tuning them on duplicated single-view input before comparing them against our approach.
cost-volume-bae based methods like Splatter360 and PanSplat, our approach demonstrates superior geometric accuracy and robustness in challenging scenarios. As shown in our qualitative results (Fig Fig.5 5 Fig. [aand Fig our Triplane- completion effectively handles large baselines and distorted regions (e.g., ceilings and floors) where cost-volume methods produce holes, artifacts, and inconsistent depth. Quantitatively (Tables (Tables[]and]) this advantage is apparent in two-view and especially single-view tasks, where cost-volume methods fail due to their architectural limitations. Our method also outperforms Triplane-based methods, such as OmniScene. OmniScene utilizes single, central Cartesian Triplane and processes views independently. which introduces distortion artifacts in panoramic renderings (Fig, Fig. 5] Fig, Bladd Fig and limits its multi-view fusion capabilities. In contrast, our framework's of multiple, per-camera cylindrical Triplanes,
Analysis. Compared
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Table 4: Ablation Study on Matterport3D (2.0m baseline) using two-view input. "Pixel Branch" and "Volume Branch" denote using only the respoctive branch. Rows 2-4 compare the performance of cy lindricul, spherical, and Cartesian Triplanes, Row 5 presents resulls without our RGB retrieval, and row shows the impact of the mutil Triplane strategy. Row 7 shows the result of training without curriculum.
Multi-view results 3601 extending he- yond the initial two-view (1 4m bascline) sctup to in- clude 3-view view configurations.
Table 5:
pe0 EAAtn
Views
WS-I5NB
LIPS
GPC
Miterpoct5i (2.0m)
018229
26.35
0.896
0,955
7G8
Mathod
PCC
WS-PSNR
SSIM
1PPSS
0.8937
0
28.97
36.11
0,00
0.912
0.03
0.094
2016
B3GB
Only Pixel Branch
23.21
Only Cylindrical Volume Branch
0,782
22.17
0,782
0.210
Table 6: Comparison of Model Complexity and Effi- ciency. "Inference Time"
Dully Syherical Volome Branch
0.581
19.22
0.633
0.308
the end-to la- tency for a single forward pass that culputs Goussians and renders them
Daly Canesian
Branch
0.564
16.77
Cylindrical Volume Branch (wro RGGR)
0.703
panorama.
Full (wl Muta Triplanc)
0.825
23.47
0,805
0.194
Merkel
Om
Roddlli
Splate360
Full (end to end)
0.809
23.25
0.791
0.185
Full
0.851
0,835
0.175
GT
Cartesian Triplan: Rased
Spherical Triplone Rsso!
Cylindrizal Triglane Reso!
Pinel
Figure 7: Ablation study visualizations on Matterport3D (leftmost column is ground truth). Among the dif- ferent coordinate systems for the Triplane, the Cartesian version shows significant distortion. and the spherical version struggles with distant rooms. Our cylindrical Triplane performs best. While the pixel branch is high- quality in visible areas, combining it with the Triplane branch improves reconstruction of distant regions.
combined with RGB retrieval strategy and dedicated fusion mechanism in the pixel branch, yields improved rendering quality and geometric fidelity, particularly in the two-view setting.
4.2 ABLATION STUDIES
Effectiveness of Key Components. As shown in Table and Fig. 7 our ablation study confirms our key design choices. Our staged training curriculum outperforms using either the pixel and volume branch alone, as well as an end-to-end training approach. The end-to-end method, which emphasizes the pixel branch due to its stronger performance in non-occluded areas, often results in an undertrained volume branch and overall subpar results. When examining the volume branch, the cylindrical Triplane consistently outperforms its Cartesian and spherical counterparts, confirming it as the more effective geometric representation for panoramic scenes. Lastly, we also show that our RGB retrieval mechanism and multi-Triplane strategy outperform OmniScene's 's original Triplane method (color decoded directly from volume features and only one Triplane at the center of all input views), mainly because our approach better preserves tigh-frequency image details and effectively manages frami- inputs.
Multi- View Input. As shown in Table 5] to demonstrate scalability, we fine-tune our two-view model for three- and four-view tasks. The results show that the quality of novel view synthesis improves the number of input views increases, confirming the effectiveness of our framework in a multi-view setting. We don't It compare methods like OmniScene or PanSplat, as their architectures are locked a fixed number of input cameras and can't be fine-tuned without retraining from scratch.
Model Efficiency. As shown in Table 6 by replacing the expensive cost volume with an efficient Triplane representation and avoiding heavy feature extractors such as DINO (Oquab et al., 2023) or U-Net (Ronneberger et al. 2013), our lightweight design is more efficient than competing methods.
4.3 VALIDATION AGAINST GROUND TRUTH DEPTH
We primarily used PCC for geometric evaluation (Tables 22 and dense GT depth is unavail- able for the Replica, Residential, and 360Loc test splits. To eliminate metric bias, we evaluated against GT depth on Matterport3D, as shown in Table 7 The results confirm: (1) Consisteney:
CylinderSplat consistently outperforms baselines across standard metrics (AbsRel. RMSE, 81), val- idating our PCC conclusions. (2) Superiority without Supervision: Unlike competitors (e.g., Splat-

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Table 7: Quantitative evaluation of geometric accuracy against Ground Truth (GT) depth on the Matterport3D
dataset.
Matterport3D (2.0m)
Matterpon: (1.5m)
Matterport3D (1.0m)
Method
AbsRel +
RMSE
81 1
AbsRel
RMSE +
81
AbsRel +
RMSE
81 1
OmniScene
0.36
0.77
0.37
0.34
0.72
0.44
0.17
0.38
0.79
Splatter360
0.47
0,91
0.28
0.40
0.71
0.35
0.17
0.40
0.75
PanSplat
0,41
1.08
0.11
0.40
1.07
0.13
0.39
1.06
0.14
Ours
0.20
0.48
0.75
0.17
0.41
0.80
0.11
0.30
0.89
Imput
PanSplat
OmniScone
Splatter360
Ours
Figure 8: Qualitative comparison the real-world outdoor 360Loc dataset for the two-view input task with 1.4m baseline. We highlight specific regions, including ceilings, curved walls, and floors. As observed, our method maintains high rendering fidelity even challenging non-Manhattan environments.
ter360, PanSplat) that require GT supervision, our method relies solely on RGB images. Outper- forming fully supervised basclines demonstrates genuine geometric robustness.
CONCLUSION
In this work, we introduce CylinderSplat, a new feed-forward framework for panoramic 3DGS. Our method combines flexible, attention- based pixel branch for high-fidelity reconstruction with geometrically aware volume branch for robust completion of occluded regions. The main contribu- tion of our cylindrical Triplane a representation that offers a better for the unique geometry of 360" panoramic images than previous Cartesian spherical approaches, enabling better handling of both synthetic and real-world environments with varying numbers views as input. One area for future improvement is fusion mechanism, which is currently simple concatenation that may not always produce perfectly seamless completions. We will aim to develop more advanced fusion techniques reduce redundant Gaussians improve the quality of the fused reconstruction.
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REPRODUCIBILITY STATEMENT
The implementation details of our model are provided Section 3
with training settings and eval- Additional ablation studies are included in the 4.2 and Appendix [.2 B.1 B.3 to clarify the effect of individual components. We promise to
uation protocols provided in Section
4 and Appendix B
release both the dataset and the code facilitate reproducibility.
ACKNOWLEDGE
The authors grateful for the valuable comments and suggestions by the reviewers and ACs. This work was supported by NSFC (62406194) 194), Shanghai Frontiers Science Center of Human-centered Artificial Intelligence (ShangHAI), MoE Key Laboratory of Intelligent Perception, HPC Platform of ShanghaiTech University and Human-Machine Collaboration (KLIP-HuMaCo), A part of the experiments of this work were supported by the core facility Platform of Computer Science and Communication, SIST, Shanghai'Tcch University.
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A STATEMENT ON THE USE OF LLM
The writing and polishing of this manuscript were greatly supported by the Large Language Model (LLM) Gemini 2.5 Pro. The LLM helped improve the language quality, including fixing grammar, refining sentence structure, rephrasing for clarity and conciseness, and ensuring consistent terminol- ogy and overall readability.
It is essential to clarify that the LLM's role was strictly limited linguistic enhancement and did not involve any part of the scientific process. All intellectual and conceptual contributions -such as research design, methodology, data analysis, and result interpretation are entirely credited to the human authors. After any Al-assisted editing, the authors carefully reviewed and manually revised the text to ensure accuracy and fidelity to their original intent. The authors take full responsibility for all content, scientific accuracy, and ethical standards of this manuscript.
B IMPLEMENTATION DETAILS
Training and Supervision. To ensure broad applicability, we utilize depth maps from the UniK3D (Piccinelli et al. 2025) foundation model, which serve as both the ground truth for su- pervision and the input geometric prior for our pixel branch. Additionally, for calculating the PCC metric, we use depth from DepthAnywhere (Wang& & Liu, (024) as our reference ground-truth depth. All experiments were conducted on two NVIDIA RTX 4090 GPUs with batch size of 2, and the in- put image resolution is consistently 512 x 1024. Our training process begins with the Matterport3D dataset, where we utilize our full three-stage curriculum. We first train the pixel branch, then the volume branch, and finally both branches jointly, with each stage lasting 10 epochs. Subsequently, for the 360Loc dataset, we fine-tune the model for an additional 10 epochs, specifically during the final joint-training stage. It is important to note that all other fine-tuning experiments mentioned in this paper also follow this joint-tuning protocol; the full three-stage curriculum is used only for the initial pre-training phase. For optimization, we use the AdamW (Loshchilov Hutter] 2017) optimizer with a learning rate of 1 x 10-4 and a cosine decay schedule.
Triplane Configuration and Initialization. Our cylindrical Triplane defaults the configuration of (N,, N2, No) = (16, 64, 128) and (Nr N .N²) = (8, 32, 64) for the (r, 2, I) axes, covering a 10m radius and height. A key aspect of our multi- Triplane strategy adapts to the scene's 's content. For synthetic scenes where a static scene dominates, we initialize each local Triplane by projecting feature points from all camera views that fall within its cylindrical volume. This serves to enrich the scene information within each Triplane. In contrast, for real- world scenes where dynamic objects like pedestrians and vehicles are unavoidable, we initialize each Triplane using only the feature points from the camera at its center. This avoids introducing noise from dynamic objects captured in other views.
the initialization process, which
Direct Panoramic Rendering. During the rendering stage, unlike prior feed-forward methods that render six cubemaa faces and stitch them into a panorama, we employ a 3DGS rasterizer designed specifically for panoramas. This allows to render the full equirectangular image in a single pass, significantly improving our rendering speed.
In the following sections, we provide more detailed analysis of our choices regarding Triplane resolution, the initialization strategies, and our direct rendering approach.
B. ABLATION ON TRIPLANE RESOLUTION
To justify the configuration of our cylindrical Triplane, we conducted a detailed ablation study on the sampling hyperparameters. Specifically, we examined the grid resolutions along the (r, 0) axes for two distinct stages: NN, Ne), utilized during Cross-Plane Attention sampling, and (N,, N,,),, utilized during Triplane-to-Iman Attention sampling. To strictly isolate the im- pact of the volumetric representation, we performed novel view synthesis using only the Volume Branch, while maintaining a constant physical bounding volume (10m radius, 360 azimuth, and 10m height).
We adapt a controlled variable approach to analyze the efficiency trade-offs: first, we fix (N,, Nú, N2) (8, 32, 64) while varying (N,, No, No); subsequently, we fix (N,, N₂, No) at
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(16, 64, 128) while varying (N,, Ny. .N). As presented in Table the results demonstrate clear trend: while increasing sampling density consistently improves reconstruction quality, it imposes corresponding penalty GPU memory usage and inference latency. Consequently,
selected the configuration of (N,, N2, No) = (16, 64, 128) and (Ny, N₂, N2) (8, 32, 64) (highlighted in bold), this combination strikes the optimal balance between performance fidelity and computa- tional efficiency.
Table 8: Ablation study on sampling hyperparameters ((N,, N2, No) and (N; No, Ng) on the Matterportä (2.0m baseline) dataset with only the volume branch.
Configuration (r", 2,
PCC f
WS-PSNR t
SSIM f
LPIPS
Memory
Time
(Nr, N., Na) = (16, 32, 64)
0.72
21.18
0.71
0.24
15GB
0.26s
(N,, Na) (16, 128)
0.78
22.17
0.78
0.21
17GB
0.29g
NN, No) (16, 128, 256)
0.80
23.88
0.82
0.19
25GB
0.34s
(N N2) = (8, 16, 32)
0.77
22.08
0.73
0.28
16GB
0.27s
(N), Nº, p²)
(8, 32, 64)
0.78
22.17
0.78
0.21
17GB
0.29s
(Np, N. No) (8, 64, 128)
22.46
0.80
0.21
18GB
0.31s
B.2 TRIPLANE INITIALIZATION STRATEGIES
For the distinct domains static and dynamic scene reconstruction, we employ two different Tri- plane initialization strategies, illustrated in Fig. 9 The first strategy, for static scenes, maximizes information by aggregating feature points from all camera views. The second, for dynamic scenes, mitigates noise by using features only from the corresponding camera view. We conducted com- parative experiment (Table 9) to validate this design choice. The results confirm that the all-view aggregation strategy (Fig. Πa) is optimal for datasets composed primarily of static scenes (e.g., Matterport3D), while the single-view strategy (Fig. bb) is superior for datasets with dynamic ele- ments (e.g., 360Loc).
candidate
candidate
Camera A's common Fenture Points
Camera B's common Feature Points
Camera Center
Cylindrical Triplac
(4) For Static Scenes
(b) For Dynamic Scenes
Figure 9: Visualization of our Triplane initialization strategies. In this figure, blue triangle represents Camera A, with blue circles denoting its corresponding feature point cloud. A yellow triangle represents Cam- B with its yellow point cloud. We construct local cylindrical Triplane at each camera's 's location and initialize by projecting feature points onto its three planes. (a) For static scenes, the Triplane for Camera is formed by aggregating all candidate feature points that fall within its volume, regardless of their origin (both blue and yellow points). (b) In contrast, for scenes with dynamic objects. the Triplane for Camera A formed exclusively by feature points from its view (only blue points). This strategy prevents dynamic inconsistencies from view (e.g., yellow points from Camera B) from corrupting another view's Triplane with noise.
Table 9: Comparison of different Triplane initialization strategies on static (Matterport3D) and dynamic (360Loc) datasets.
Dataset
Matterport3D (2.0m baseline)
360Loc (avg.
1.40m baseline)
Method
PCCT
WS-PSNRT
SSIM
LPIPS
PCCT
WS-PSNRT
SSIM
LPIPS
Shared Initialization
0.851
23.76 23.24
0.835
0.175
0.831
27.33 28.35
0.841
0.107
Isolated Initialization
0.847
0.796
0.197
0.883
0.896
0.095
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B.3 COMPARISON OF 3DGS RENDERING METHODS
Prior feed-forward methods for panoramic NVS typically rely on an indirect rendering pipeline: they first render six perspective views form cubemap, which is then stitched together create the final panoramic image. In contrast, our method utilizes a 3DGS rasterizer (Li et al.] (025). designed explicitly for panoramas, which enables us to render the full equirectangular image directly. This approach is more efficient and requires two core modifications to the standard splatting pipeline.
First, to project the 3D Gaussians into the 2D panoramic image space, we replace the standard pin- hole projection with an equirectangular projection function. For 3D Gaussian centered at (x. y, 2), its corresponding 2D pixel coordinate (u, v) in a panorama of resolution H W calculated as:
W
= (atan2(æ, 2) T)
(8)
2m
V [alan2(y, V + 22) + 7
Second, the Jacobian of this projection function, which used transform the 3D Gaussian covari- ances into 2D, must be updated. The general form of the Jacobian J; for Gaussian i is the matrix of partial derivatives of the pixel coordinates (u;; 1/) with respect to the 3D world coordinates (ar, y, z):
Ou,
Ou
Ou;
Zz
dy
Ōz
J.
(10)
dz
du:
dv.
Oz
Oy
0z
For our specific panoramic projection, this results in the following specialized Jacobian:
W
2
W
T2
0
2T
2
2T
x2 + 2
J, =
(11)
H1
T,3h
H
7:3/s
r
where = /23 + yf + 27. Using these updated calculations allows us to render panoramic views directly. Furthermore, as validated by the experiment Table [O] while the rendering quality of our direct panoramic 3DGS is comparable to the indirect method of stitching six pinhole views (cubemaps). our direct approach is faster.
Table 10: Comparison different rendering methods on the Matterport3D dataset for the two-view input task, evaluating quality metrics and inference time.
Dutasct
MaterportID
Baseline
1.5m
Inference Time
PCC1
PSNRT
SSIMI
LPIPS.
PSNR1
LPIPS)
SSIM1
LPIPS]
CabeMaa
0815
29.71
0.822
0.171
0.853
25.51
0.856
0.129
0.96
78 96
0.995
0.076
0.3%
Panorama Ganssian
0351
23.76
0,835
0.175
0.967
25.91
0.73
0.128
0.923
28.89
0937
0,081
C MOTIVATION FOR CYLINDRICAL TRIPLANES
As illustrated in Fig. T0 our cylindrical Triplane is motivated by four key advantages over Cartesian and spherical alternatives.
Even though flat, box-like Cartesian plane (as in Fig. 10 a)) works well for structured environments like buildings with straight walls, it introduces significant problems when dealing with 360-degree panoramic images. When information from a flat grid is projected onto a panorama, the image becomes stretched and blurry. The ability of this plane to represent details accurately quickly diminishes when the plane is too close or too far from the camera. Points on the same Cartesian plane, when projected onto a panoramic image, suffer from severe distortion (see Fig. c)). Furthermore, when mapping 360-degree information back onto these flat planes, the
Cartesian Triplane.
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Cartesian system's lack of omnidirectional awareness leads to many feature points from different panoramic pixels projecting onto the same grid cells of the Triplane. This results in messy and unevenly distributed information on the plane (see Fig. IO].)..
Spherical Triplane. A spherical coordinate system naturally aligns with cquirectangular pixel grids, offering theoretically perfect uniform sampling where every feature corresponds equal portion of the 360 view (as shown in Fig. I..).)). However, this geometric elegance poorly fits real-world "Manhattan-world" scenes (Fig. a))a)). Spherical Triplanes struggle represent flat surfaces, such as floors and ceilings, and exhibit severe distortion at their poles.
Cylindrical Triplane. The cylindrical coordinate system provides compelling balance. As shown in Fig. 10 a). effectively models Manhattan-wooll structures (floors, ceilings, walls) without the severe distortions produced by the spherical system. Its sampling pattern (Fig. [0fc)). while per- fectly uniform, is far more stable than Cartesian. This distribution beneficially prioritizes top/bottom features small radii and central panoramic regions at large radii, which aligns well with real- world panoramic depth distributions, where the central region typically exhibits the greater depth and the top/bottom regions smaller. Furthermore, projecting panoramic feature clouds onto a cylindrical Triplane results in relatively uniform feature distribution (Fig. bO)..).
Finally, qualitative rendering comparisons using only the volume branch (Fig. TO(d) demonstrate that cylindrical Triplane consistently produces novel views with significantly more detail and fewer distortion artifacts than Cartesian and spherical counterparts.
(a) Fitting Real- Scuees
(b) Triplane Init
XY Plane
ZY Plane
80 Plane
R-D Plane
eZ Plane
RZ Plane
Fylinder-wold
Cintosire-Prood
X
Sphero-haaed
Cylinder-bosed
(<) Toplane-to-Image Artention
(4) Qualitarive Comparison of Different Triplane
Cameesin- based
Sphere-based
Cylinden-based /
Spitere
Cylinder
Figure 10: Visualizing the advantages of the cylindrical Triplane. (a) The shape of the sampling volumes for cach coordinate system when fitting typical synthetic scene, highlighting their geometric alignment. Feature distribution during Triplane initialization. At this stage, panoramic feature point clouds are projected onto the Triplane surfaces. The Cartesian system suffers from heavy point overlap. where many distinct 3D feature points are projected to the same grid cells. In contrast, the spherical and cylindrical systems achieve much
distribution of features. (c) Projection patterns for Triplane-to- loallaee-o--mage Attention. We visualize how sample points from each Triplane's volume project onto the panorama across six maps for each coordinate system, ordered by increasing distance from the origin. For the Cartesian system (left). maps show that on a Cartesian plane, as this plane moves farther from the origin, its panoramic projection exhibits limited field of view and severe distortion. For the spherical system (middle). maps il- lustrate that spherical surface, with increasing sphere radius (distance from origin), its projection remains consistently uniform across panorama. Finally, for cylindrical system (right), maps display that on a cy cylidcrical surface, as its radius increases, the projection remains relatively uniform but naturally focuses more on the panoramic center, better aligning with real-world depth distributions. (d) Qualitative rendering compar- ison using only the volume branch, demonstrating the superior performance of the cylindrical representation in view synthesis.
gross-attention. This shown
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D ABLATE DIFFERENT DEPTH PRIORS (E.G., DEPTHANYWHERE. ZOEDEPTH)
To demonstrate the robustness of our framework, we substitute our default UniK3D (Piccinelli et al.) 2025) prior with DepthAnywhere (Wang & Liu 2024 and UniFuse (Jiang al. (021). We specif- ically include UniFuse as it serves as the depth prior for both PanSplat and Splatter360.
Table II demonstrates that method maintains consistent performance advantages across different depth priors. Even when utilizing the exact same UniFuse prior as PanSplat and Splatter360, our approach yields superior quality. Crucially, we achieve these results without ground truth (GT) depth supervision; in contrast, both Splatter360 and PanSplat rely on UniFuse for priors and additionally require GT depth as supervision during training.
Table 1I: Different depth prior on Matterport3D 2.0m baseline.
Method
AbsRel
RMSE 4
81 f
PCC f
PSNR
SSIM f
LPIPS +
OmniScene
0.36
0.77
0.37
0.73
22.75
0.71
0.24
Splatter360
0.47
0.91
0.28
0.68
21.31
0.74
0.28
PanSplat
0.41
1.08
0.11
0.71
20.56
0.77
0.26
Ours (DepthAAywheree
0.34
0.66
0.60
0.78
23.08
0.78
0.22
Ours (UniFuse)
0.31
0.64
0.62
0.78
23.31
0.81
0.21
Ours (UniK3D)
0.20
0.48
0.75
0.85
23.76
0.83
0.17
E DERIVATION AND VALIDITY OF SCALE TRANSFORMATION (S')
1. Rotation (R). First, we clarify that Rotation (R) does not undergo coordinate transformation. Consistent with standard 3DGS, our MLP predicts rotation (as quaternions) directly in the global Cartesian coordinate system. The coordinate transformation (Eq. & 4) applies only to the local position offset (ō and scale (Steaat.
Local Position (dioca) and Scale (Socal). Although S" involves local approximation, this design strictly follows the standard "volume-bounded" optimization principle used in existing grid- based 3DGS methods. As illustrated in Fig. 2(d) our paper, methods like OmniScene constrain each Gaussian primitive within fixed volume element ("Cartesian Volume" in Fig. 2(d)) defined by the Triplane grid resolution (e.g., dimensions of 25,. 26, 262). The learable position offsets and scales are constrained within this grid (e.g., tō,, to. tz). This constraint is critical for ensuring training stability.
We apply this same principle our cylindrical representation. As shown in the "Cylindrical Vol- ume" of Fig. d)) our grid units are curvilinear frustums defined by (26,,, 26g, 252). Consequently, network predicts local offsets and scales (owcal Sieaa) restricted within these local bounds (e.g..
ł8,, tōg = Since the standard 3DGS rasterizer accepts only Cartesian inputs, we must trans- form these locally predicted parameters into the global Cartesian frame. This necessitates the coor- dinate transformation for positions (Eq. 3) and the Jacobian-basee transformation for scales (Eq. 4).
Finally, we perform an additional ablation study on Matterport3D (2.0m baseline) comparing our Jacobian-baa based scaling with predicting Cartesian scales directly within the cylindrical triplane. The results confirm that respecting the local cylindrical geometry through our transformation outper- forms the alternative.
Table 12: Ablation study on scale transformation strategy (Matterport3D, 2.0m baselinc).
Method
abs_rel +
rmse 1.
deltal
PCC
PSNR
SSIM 1
LPIPS 1
Ours (Cartesian Scale)
0.22
0.55
0.73
0.82
23.54
0.82
0.18
Ours (Cylinder Scale)
0.20
0.48
0.75
0.85
23.76
0.84
0.18
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F PERFORMANCE ON
REAL-WORLD SCENARIOS
To substantiate our SOTA claim real-world settings, we conducted two additional evaluations focusing on challenging wide-baseline scenarios where geometric completion is most critical:
1. 360Loc with wider baselines (3.0 m and 4.5 m).
2. A new large-scale real-world dataset curated from Google Street View (Kansas City), comprising 8,500 sequences with extreme baselines (20-35 m) and fewer dynamic ob- jects.
As reported in Tables although the gains at a short baseline (1.4 m) are moderate, our advan- tage increases substantially the baseline widens. On the Kansas dataset (20 m+ baseline), we outperform the strongest competitor (OmniScene) by +3.95 dB in WS-PSNR. This confirms that CylinderSplat excels in sparse-view synthesis under challenging real-world conditions.
Table 13: Quantitative comparison on wide-baseline real-world scenarios. We report results on the Kansas dataset (extreme 20m -0m baseline) and 360Loc dataset with increasing baselines (4.5m and 3.0m). Our method consistently outperforms baselines, with the performance gap widening the difficulty increases.
Kansus Dataset (20m- (20m-30m) 30m)
360Lxc (4.5m Baseline)
360Lac (3.0m Baseline)
Method
PCC1
PSNR
SSIM
LPIPS 4
PCC
PSNR
SSIM 1
LPIPS
PCC
PSNR
SSIM
LPIPS L
OmniSoenc
0.55
17.08
0.44
0.35
0.84
0.53
0.41
0.86
21.83
0.66
0.30
Splatrer360
0.39
16.21
0.36
0.47
0.80
19.93
0.62
0.32
0.82
22.65
0.71
0.24
PanSplat
0.07
15.02
0.29
0.61
0.10
21.03
0.69
0.28
0.11
23.47
0.75
0.22
Ours
0.63
21.03
0.67
0.21
0.86
22.68
0.73
0.22
0.86
24.50
0.79
0.18
G TRAINING COMPLEXITY AND EFFICIENCY ANALYSIS
We clarify that the three-stage training is required only for the initial training on the Matterport3D dataset ensure robust initialization of the independent Pixel and Volume branches. For all other datasets (e.g., 360Loc, Kansas), we only train the third stage (Joint Training). fine-tuning from the weights trained on Matterport3D.
As shown in Table 14
1. On Matterport3D: Even with separate stages, the combined per-iteration time of Stage and Stage 2 (0.81s+ 1.13s 1.94s) is still faster than single iteration of PanSplat (2.17s). We need 10 more epochs for the final joint stage.
2. On Other Datasets (e.g., 360Loc): Since we only execute the third stage, our training time per iteration (1.98s) is faster than all competing methods (PanSplat 2.17s, Splatter360 2.89s, OmniScene 3.23s).
Table 14: Comparison of training efficiency (time per iteration and epochs) against baselines.
Method
Training Time (per iteration)
Training Epochs
OmniScene
3.23s
Splatter360
2.89s
10
PanSplat
2.17s
10
Ours (Stage 1: Pixel Branch)
0.81s
10
Ours (Stage 2: Volume Branch)
1.13s
10
Ours (Stage 3: Joint Training)
1.98s
H ANALYSIS OF PANORAMIC ARTIFACTS: SEAMS AND POLES
1. Seams: The Cylindrical Triplane is logically circular in the dimension; queries at
and 0 = 2nr access the exact same physical location in the coordinate system, rendering
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method naturally seamless. To quantify this, evaluate the Left-Right Consistency Error (LRCE) (the mean pixel difference between the left and right boundaries). As shown in Table [3| our method achieves error that is order of magnitude lower than competing methods (e.g., 0.025 vs. PanSplat's 0.088), confirming superior continuity.
2. Poles:
visualized Figure T0 of the supplementary material, our Cylindrical Tri- plane leverages geometric prior that optimally aligns with real-world depth distributions: it prioritizes sampling at poles for regions with small radii (close to the camera) while focusing on the central horizon for regions with large radii (far from the camera). Fur- thermore, by maintaining uniform resolution along the Z-axis, our representation ensures consistent detail for the zenith and nadir regions, allowing the geometric representation itself robustly handle polar areas as shown in (Fig. Fig. Fig. 6 and Fig
Table 15: Left-Right Consistency Error (LRCE) 4. Lower values indicate better
Table 16: Quantitative comparison advanced Gaus- sian fusion strategies the Matterport3D dataset (2.0m baseline).
continuity at panoramic boundaries.
Mothod
Mp3D (2.0m)
Mp3D (1.5m)
Mp3D (1 (.h))
Method
PCC
WS-PSNR
SSIM + LPIPS
OmniSoone
PanSplat
Splatte3300
0.291
0.165
0.137
0.325
0.119
0.146
0.299
0.088
Depth Pruning
Density Clustering
0.87
0.84
22.52
23,91
0.75
0.87
0.15
0.24
Ours
0.025
0.024
0.023
Ours (Original)
0.85
23.76
0.83
0.17
I ANALYSIS OF ADVANCED GAUSSIAN FUSION STRATEGIES
To explore fusion strategies beyond simple concatenation, we implemented and evaluated both den- sity clustering and depth-guided pruning.
As shown in Table T6. our findings are as follows:
We attempted to merge tightly packed Gaussians by learning confi- dence weights via softmax mechanism. However, found that the network struggled to learn reliable confidence scores for fusion, leading to visual artifacts (distortions and holes) and performance drop compared
Density Clustering:
original concatenation baseline.
Depth-Guided Pruning: Conversely, this strategy proved effective. By reducing the opacity of Gaussians that deviate significantly from the predicted depth and pruning low- confidence primitives, achieved improvements in both geometric accuracy and render- ing quality.
J RE-EVALUATING OMNISCENE WITH DIRECT PANORAMIC RASTERIZER.
To isolate the impact of the rendering pipeline from the geometric representation, re-evaluate OmniScene using our direct panoramic rasterizer. As shown in Table 17 replacing the cubemap renderer with direct rasterizer yields comparable overall performance. This mirrors the con- clusion in Table T0 where we demonstrated that switching CylinderSplat to a cubemap renderer had negligible impact on quality. Thus, conclude that the direct rasterizer primarily contributes to inference speed, while the superior reconstruction quality is driven by our Cylindrical Triplane representation.
Table 17: Comparison of OmniScene performance using Cubemap vs. Direct Panoramic Rasterizer on Matterport3D 2.0m baseline.
Method
AbsRel J.
RMSE J
8, f
PCC
PSNR t
SSIM f
LPIPS
Inference Time
OmniScene (Cubemap)
0.37
0.65
0.53
0.73
22.75
0.71
0.24
0.48s
OmniSeene (Direct Raster)
0.29
0.63
0.61
0.76
22.19
0.76
0.24
0.44s
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K
LIMITATIONS
Our method encounters challenges in parts of the 360Loc dataset, where unavoidable dynamic ele- ments (e.g., the photographer at the nadir, moving pedestrians). Since we do explicitly optimize for transient objects, these can cause ghosting artifacts in the synthesized views. We have visualized these specific cases in the Fig noting that while our method exhibits ghosting, this issue is equally prevalent in competing methods.
L FURTHER SCENE VISUALIZATIONS
We provide additional scene visualizations by comparing our method with others on both two-view and single-view tasks, examining rendered RGB images and depth maps as shown in Fig. TT and Fig. 12 Compared to Triplane-based methods like OmniScene, which utilizes a Cartesian Triplane, our approach demonstrates superior performance. OmniScene often produces noticeable artifacts, distortions, and loss of detail in rendered RGB and depth maps, particularly near the bottom of the panorama (e.g., ground regions). Specifically, its depth maps frequently exhibit striped artifacts near the ceiling and floor. Furthermore, compared CostVolume-basee and PanSplat), our approach avoids the common issues of holes and distortions that arise when input view spacing is large. Our method excels minimizing distortion while effectively filling in the occluded regions, offering more complete and accurate reconstruction.
methods (including Splatter360
M VISUALIZATIONS OF NON-MANHATTAN AND OUTDOOR SCENES
We include additional visualizations in the supplementary material, specifically targeting outdoor scenes from the 360Loc dataset (Fig. (8) and complex urban/forest environments from the Kansas dataset (Fig. 13). These qualitative results demonstrate that even in scenarios featuring curved struc- or unstructured outdoor geometry, our method maintains robust performance comparable to fatt--of-the-ar baselines.
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GT
Splatcer360
PanSplat
OmniScenc
Ours
Figure 11: Qualitative comparisons on synthetic datasets for the two-view input task, with an input view baseline of 2.0m. The first column shows the rendered RGB image for novel view, and the second column displays its corresponding depth map. The third and fourth columns provide zoomed-in in results of the RGB image and depth map, respectively.
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GT
Splatter360
PanSplat
OmniScene
Ours
Figure 12: Qualitative comparisons synthetic datasets for the single-view input task, where the dis-
tance between the input and output views is 1.0m. The first column shows the rendered RGB image for novel view, and second column displays its corresponding depth map. The third fourth columns provide zoomed-in results of the RGB image and depth map, respectively.
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Input
Splaster360
PanSplat
OmniScene
Our
Figure 13: Qualitative comparison the real-world outdoor Kansas Dataset for the two-view input
task. The distance between the input views approximately 20-30m, making highly challenging scenario. Although some blurriness remains, our method significantly outperforms baselines. demonstrating the elfectiveness of CylinderSplat for sparse-view synthesis under demanding weal-world conditions.
25


Reviews:
[{"review_id": "gc2dzLO3Tl", "reviewer": "Reviewer_K1rt", "summary": "The authors propose a panoramic novel view synthesis method using 3D Gaussian Splatting (3DGS). They introduce a cylindrical triplane representation combined with a dual-branch design: an attention-based, cost-volume-free pixel branch that parameterizes Gaussians from the inputs, and a per-camera local cylindrical-triplane volume branch that handles occlusions. On both synthetic and real datasets, the method achieves competitive image quality (WS-PSNR, SSIM, LPIPS) and geometry metrics (PCC) with strong efficiency.", "questions": "Using a foundation model–based depth prior as a reference for geometry evaluation raises concerns about circular validation and fairness in comparisons.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Originality. Introduces a cylindrical triplane representation specifically designed for panoramic 3D Gaussian Splatting. Proposes a three-stage curriculum training strategy (pixel, volume, joint) that significantly enhances reconstruction in occluded and sparsely viewed regions.\n\n2. Quality. Provides comprehensive evaluations across multiple panoramic and indoor datasets with consistent improvements over prior methods. Includes thorough ablation studies demonstrating the effectiveness of the cylindrical triplane and each component.\n\n3. The paper is well-organized and easy to follow.\n\n4. Significance. Effectively addresses the blurriness and occlusion artifacts found in previous sparse-view panoramic methods.", "weaknesses": "1. The method relies heavily on a strong depth prior, but the paper does not analyze its robustness or potential failure cases.\n2. The domain bias is significant—experiments focus primarily on indoor, Manhattan-world panoramas. Analysis of non-Manhattan or outdoor environments would strengthen the generality claims.\n3. The paper does not discuss or address artifacts near seams and poles, which are common in panoramic representations and could impact real-world performance.", "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": "4WapaH1QsM", "reviewer": "Reviewer_5C2X", "summary": "The paper proposes CylinderSplat, a feed‑forward 3D Gaussian Splatting framework for 360 panoramas. The system has two branches: (i) a pixel branch that uses attention to aggregate multi‑view features and predict Gaussians for well‑observed pixels, and (ii) a volume branch that introduces per‑camera cylindrical triplanes to complete occlusions and sparsely observed regions. A three‑stage curriculum trains pixel, then volume, next joint fine‑tuning. Color for volume‑branch Gaussians is obtained by RGB retrieval from input panoramas with a simple visibility weighting derived from a depth prior. The paper also implements direct equirectangular 3DGS rasterization without doing cubemap rasterization.", "questions": "- Please provide a derivation (or empirical validation) showing why S' is appropriate for anisotropic Gaussians in 3DGS.\n\n- Can you report geometry vs. ground‑truth depth on any subset (e.g., Replica), or provide alternate metrics less tied to a particular depth estimator?\n\n- How does the method fare on scenes with curved walls or outdoor panoramas where cylindrical alignment is less appropriate?\n\n- Have you tried a learned Gaussian deduplication/merging module (e.g., confidence‑weighted thinning or non‑maximum suppression in splat space) rather than concatenation?\n\n- Since you introduce a direct panoramic rasterizer, can you re‑evaluate OmniScene with the same rasterizer (rather than a cubemap adaptation) to isolate representation vs. rendering pipeline effects?\n\n- Please specify precisely what Table 6 “Inference Time” measures\n\n- Since UniK3D underpins depth supervision and visibility, can you ablate different depth priors (e.g., DepthAnywhere, ZoeDepth) to show robustness?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- a practical pipeline that avoids heavy cost‑volumes while supporting a variable number of input panoramas via attention.\n\n- Cylindrical triplane is a sensible geometric choice for Manhattan‑style scenes; the paper gives qualitative intuition and ablations showing it outperforms Cartesian and spherical alternatives in this setting.\n\n- The paper measures the effect of each key component: coordinate system, RGB retrieval, multiple per‑camera triplanes, and training curriculum.\n\n- Results are broadly stronger on synthetic datasets, and the method scales from single‑view to multi‑view inputs", "weaknesses": "- While the cylindrical triplane is well‑motivated, it can be viewed as a coordinate adaptation of established triplane+3DGS paradigms, rather than a new representation class or learning principle. Much of the performance gain appears to come from engineering side and somehow lacks of technical novelty.\n\n- The transformation from cylindrical local scales to Cartesian scales is kind of approximation without derivation—raising concerns about correctness and whether R and S are consistently transformed.\n\n- PCC is computed against DepthAnywhere (no GT), while UniK3D is used for depth supervision/priors and visibility weighting. Although the models differ, this model‑to‑model comparison weakens claims of geometric accuracy and could bias design choices toward correlating with a particular depth estimator.\n\n- On 360Loc two‑view, gains over a strong panoramic baseline are small (e.g., WS‑PSNR 28.35 vs. 28.14; LPIPS 0.095 vs. 0.127), which tempers the “SOTA” claim outside synthetic domains. This should be carefully justified.\n\n- Competitors like Splatter360 and PanSplat are designed for ≥2 views; duplicating a single view for training creates a non‑native setting that can amplify CylinderSplat’s advantage.\n\n- What exactly is counted in the “inference time” of Table 6 (one forward pass to Gaussians? including rendering of how many targets)?\n\n- Several sampling hyperparameters (e.g.,  N_r, N'_r are left unspecified in the main text; these matter for memory/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": {}}, {"review_id": "J7EfqPIs5I", "reviewer": "Reviewer_7MCk", "summary": "This paper proposes CylinderSplat, a feed-forward framework for panoramic novel view synthesis that marries a pixel branch (attention-based multi-view aggregation predicting per-pixel Gaussians without cost volumes) with a volume branch that completes occluded/sparse regions using a new cylindrical Triplane representation. The key contribution is replacing standard Cartesian triplane representations with cylindrical triplanes, motivated by the Manhattan-world assumption common in indoor/urban scenes. Features from both are trained in a three-stage curriculum and fused for direct equirectangular 3DGS rendering with an RGB-retrieval step for high-frequency detail. Unlike prior panoramic feed-forward methods that depend on multi-view cost volumes or Cartesian triplanes, the cylindrical triplane reduces distortion/aliasing and enables flexible inputs from single to multiple panoramas. Across Matterport3D, Replica, Residential, and 360Loc, CylinderSplat shows state-of-the-art image quality. Ablations show the benefit of the cylindrical triplane. The method is lightweight and fast (≈11.3M params; ~0.29s inference).", "questions": "1. What are some of the scenes/settings where the proposed method is likely to fail?\n2. How does performance degrade with larger baselines between input views (>2m)?\n3. What is the training time comparison with baselines?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The cylindrical coordinate system choice is well-justified through both theoretical analysis (Manhattan-world assumption) and comprehensive ablations showing clear advantages over Cartesian and spherical alternatives.\n2. Extensive experiments are shown across multiple datasets with thorough ablations examining triplane resolution, coordinate systems, initialization strategies, and rendering methods. Consistent improvements are shown in both single-view and multi-view settings, with particularly notable gains in geometric accuracy.\n3. Unlike prior cost-volume methods, the attention-based pixel branch handles variable numbers of input views (1 to multiple) without retraining.\n4. Good implementation details, clear mathematical formulations, and promise of code release for reproducibility.", "weaknesses": "1. Limited novelty: The core contribution is essentially a coordinate system change for existing triplane methods. The dual-branch architecture, attention mechanisms etc. seem to be borrowed from prior work.\n2. Manhattan-world assumption limitations: The method seems to be explicitly designed for environments with orthogonal surfaces (indoor/urban scenes). Applicability to natural outdoor scenes, curved structures, or non-Manhattan environments is unclear\n3. Training complexity: The three-stage curriculum (pixel branch → volume branch → joint fine-tuning) suggests potential brittleness: Table 4 shows end-to-end training is inferior.", "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": "uAdy0eOgEv", "reviewer": "Reviewer_rcos", "summary": "This paper proposes CylinderSplat, a novel feed-forward framework for panoramic 3D Gaussian Splatting. The key idea is to replace standard Cartesian triplanes with cylindrical triplanes, which are geometrically aligned with panoramic imagery and Manhattan-world structures. The method employs a dual-branch design: a pixel branch for reconstructing well-observed regions and a volume branch using cylindrical triplanes for hallucinating geometry in occluded areas. Extensive experiments across multiple panoramic datasets demonstrate state-of-the-art performance in both single- and multi-view settings, with particularly strong results in challenging sparse-view scenarios. Rich ablations validate the architectural decisions, including the cylindrical coordinate choice, RGB retrieval mechanism, and staged training strategy.", "questions": "1. Fusion strategy: Could more advanced Gaussian merging (e.g., density clustering, depth-guided pruning) further improve seamless blending between branches? Have such strategies been experimented with?\n\n2. Non-Manhattan-world scenes: How does performance degrade in highly unstructured outdoor scenes (e.g., forests, caves)?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear novelty and motivation: Introducing cylindrical triplanes for panoramic 3DGS is both intuitive and technically meaningful, improving geometric alignment and addressing limitations of Cartesian/spherical triplanes.\n\n2. Strong empirical results: The method consistently outperforms existing feed-forward panoramic reconstruction approaches in both quantitative metrics and visual quality, including single-view settings.\n\n3. Comprehensive experimental suite: Experiments span multiple synthetic and real-world datasets, various view counts, and a detailed ablation study, increasing confidence in the robustness and generalizability.\n\n4. Solid architectural design: The dual-branch structure and curriculum training pipeline are conceptually sound and empirically effective, enabling accurate pixel-aligned estimation and robust geometry completion.\n\n5. Efficient and scalable: Avoiding cost-volume construction improves efficiency, and the model flexibly supports arbitrary numbers of input panoramas.", "weaknesses": "Limited discussion on memory efficiency: Although triplane is used for compression, the method still constructs multiple local triplanes and can scale in memory with view count; a clearer complexity breakdown would help.", "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)