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CylinderSplat: 3D Gaussian Splatting with Cylindrical Triplanes for Panoramic Novel View Synthesis

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
3D Gaussian Splatting;Panoramic Novel View Synthesis;Cylindrical Triplane;Feed-forward;Multi-view Reconstruction
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∘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.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

CylinderSplat introduces a feed-forward framework for panoramic 3D Gaussian Splatting using a novel cylindrical Triplane representation aligned with 360-degree geometry. The paper sits within the 'Indoor Scene Reconstruction from Single Panorama' leaf, which contains five papers total. This leaf represents a moderately active research direction focused on extracting 3D structure from minimal panoramic input using layout or geometric priors. The sibling papers similarly exploit Manhattan-world assumptions and semantic cues for indoor environments, suggesting CylinderSplat operates in a well-defined but not overcrowded niche.

The taxonomy reveals neighboring branches addressing related challenges: 'Neural Radiance Field-Based Single Panorama Synthesis' explores NeRF-based completion strategies, while 'Pose-Based Sparse Panoramic View Synthesis' handles multiple panoramic inputs with known poses. CylinderSplat's dual-branch architecture bridges single-image and sparse-view scenarios, diverging from purely single-panorama methods by flexibly handling variable input counts. The cylindrical Triplane representation contrasts with Cartesian volumetric approaches common in perspective-view synthesis, reflecting the field's ongoing exploration of geometry-aware representations tailored to equirectangular data.

Among thirty candidates examined, none clearly refute the three core contributions. The cylindrical Triplane representation examined ten candidates with zero refutations, suggesting limited prior work explicitly combining cylindrical geometry with Triplane structures for panoramic splatting. The dual-branch framework and attention-based aggregation mechanism each examined ten candidates with similar outcomes. This pattern indicates the contributions appear novel within the limited search scope, though the analysis does not cover exhaustive literature beyond top-K semantic matches and citation expansion.

Based on the examined candidates and taxonomy position, CylinderSplat appears to offer meaningful technical novelty in adapting 3D Gaussian Splatting to panoramic data. The cylindrical Triplane and dual-branch design address specific geometric challenges underexplored in prior indoor reconstruction work. However, the limited search scope means potential overlaps in broader splatting or volumetric representation literature may exist beyond the thirty candidates analyzed.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: panoramic novel view synthesis from single or sparse views. The field divides into several major branches reflecting different input assumptions and application contexts. Single Panoramic Image-Based Synthesis focuses on reconstructing 3D scenes or generating new viewpoints from a lone 360-degree image, often leveraging geometric priors or learned depth estimation for indoor environments. Sparse Multi-View Panoramic Synthesis tackles scenarios with a small handful of panoramic captures, employing techniques such as neural radiance fields or multi-cylinder representations to interpolate novel views. Generative and Cross-Domain Panoramic Synthesis explores diffusion models and generative priors to hallucinate plausible content when input is extremely limited or comes from non-panoramic sources. Quality Enhancement and Specialized Rendering addresses post-processing challenges like low-light conditions or high-fidelity rendering, while Classical and Traditional Panoramic Stitching covers foundational mosaicking methods. Application-Specific Panoramic Systems targets niche domains such as autonomous driving or medical imaging, where panoramic views serve specialized needs. Within Single Panoramic Image-Based Synthesis, a particularly active line of work centers on indoor scene reconstruction, where methods like Pano2Room[4] and Layout-guided Panorama[5] exploit room layout constraints to infer geometry and texture from a single equirectangular image. These approaches contrast with more general outdoor or unstructured settings by relying on Manhattan-world assumptions and semantic cues. The original paper ```json[0] fits naturally into this indoor reconstruction cluster, sharing the emphasis on extracting 3D structure from minimal panoramic input. Compared to Multi-cylinder PanoSynth[15], which handles multiple overlapping panoramas, or CylinderSplat[3], which uses splatting-based rendering, ```json[0] appears to prioritize single-image scenarios and may incorporate layout or depth priors similar to neighboring works. This positioning highlights ongoing trade-offs between input sparsity, geometric assumptions, and rendering quality across the taxonomy.

Claimed Contributions

Cylindrical Triplane representation for panoramic 3D Gaussian Splatting

The authors introduce a cylindrical coordinate-based Triplane representation that naturally aligns with the geometry of 360-degree panoramic scenes and Manhattan-world structures (vertical walls and horizontal floors), replacing standard Cartesian Triplanes used in prior methods.

10 retrieved papers
CylinderSplat dual-branch feed-forward framework

The authors propose CylinderSplat, a dual-branch architecture combining a pixel-based branch for reconstructing well-observed regions with a volume-based branch using cylindrical Triplanes to complete occluded or sparsely-viewed areas, flexibly handling variable numbers of input panoramic views.

10 retrieved papers
Attention-based multi-view aggregation mechanism

The authors replace computationally expensive multi-view cost volumes with an attention-based mechanism that aggregates multi-view context, enabling flexible handling of arbitrary numbers of input views without architectural constraints or retraining.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Cylindrical Triplane representation for panoramic 3D Gaussian Splatting

The authors introduce a cylindrical coordinate-based Triplane representation that naturally aligns with the geometry of 360-degree panoramic scenes and Manhattan-world structures (vertical walls and horizontal floors), replacing standard Cartesian Triplanes used in prior methods.

Contribution

CylinderSplat dual-branch feed-forward framework

The authors propose CylinderSplat, a dual-branch architecture combining a pixel-based branch for reconstructing well-observed regions with a volume-based branch using cylindrical Triplanes to complete occluded or sparsely-viewed areas, flexibly handling variable numbers of input panoramic views.

Contribution

Attention-based multi-view aggregation mechanism

The authors replace computationally expensive multi-view cost volumes with an attention-based mechanism that aggregates multi-view context, enabling flexible handling of arbitrary numbers of input views without architectural constraints or retraining.

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