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

• URL: http://arxiv.org/abs/2603.05882v1
• Date: Fri, 06 Mar 2026 04:04:20 GMT
• Title: CylinderSplat: 3D Gaussian Splatting with Cylindrical Triplanes for Panoramic Novel View Synthesis
• Abstract summary: CylinderSplat is a feed-forward framework for panoramic 3DGS.<n>It achieves state-of-the-art results in both single-view and multi-view panoramic novel view synthesis.
• Score: 53.231427733457224
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• ReconSplat: Generalizable 3D Scene Reconstruction Beyond Observed Views [9.257489669927624]
  ReconSplat is a feed-forward model for 3D scene reconstruction.<n>It provides both geometrically aligned novel views and sharp depth estimates.<n>It produces both photorealistic novel views and accurate depth maps on real-world benchmarks.
  arXiv  Detail & Related papers  (2026-08-28T21:53:58Z)
• Pano2World: End-to-End 3D Generation via Unified Multi-View Sequences [9.89397063309918]
  We present Pano2World, which takes a single indoor panorama as input and directly outputs a persistent, explorable 3D Gaussian scene.<n> Experiments demonstrate that Pano2World significantly outperforms existing methods on the multi-position panoramic novel-view synthesis benchmark.
  arXiv  Detail & Related papers  (2026-07-01T11:54:02Z)
• OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting [78.70702961852119]
  OracleGS reconciles generative completeness with regressive fidelity for sparse view Gaussian Splatting.<n>Our approach conditions the powerful generative prior on multi-view geometric evidence, filtering hallucinatory artifacts while preserving plausible completions in under-constrained regions.
  arXiv  Detail & Related papers  (2025-09-27T11:19:32Z)
• CylinderPlane: Nested Cylinder Representation for 3D-aware Image Generation [30.123631491028352]
  CylinderPlane is a novel implicit representation based on Cylindrical Coordinate System.<n>Our representation is agnostic to implicit rendering methods and can be easily integrated into any neural rendering pipeline.
  arXiv  Detail & Related papers  (2025-07-21T13:28:59Z)
• You Need a Transition Plane: Bridging Continuous Panoramic 3D Reconstruction with Perspective Gaussian Splatting [57.44295803750027]
  We present a novel framework, named TPGS, to bridge continuous panoramic 3D scene reconstruction with perspective Gaussian splatting.<n>Specifically, we optimize 3D Gaussians within individual cube faces and then fine-tune them in the stitched panoramic space.<n>Experiments on indoor and outdoor, egocentric, and roaming benchmark datasets demonstrate that our approach outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-04-12T03:42:50Z)
• LinPrim: Linear Primitives for Differentiable Volumetric Rendering [51.56484100374058]
  We introduce two new scene representations based on linear primitives.<n>We present a different octaiableizer that runs efficiently on GPU.<n>We demonstrate comparable performance to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-01-27T18:49:38Z)
• Splatter-360: Generalizable 360$^{\circ}$ Gaussian Splatting for Wide-baseline Panoramic Images [52.48351378615057]
  textitSplatter-360 is a novel end-to-end generalizable 3DGS framework to handle wide-baseline panoramic images.<n>We introduce a 3D-aware bi-projection encoder to mitigate the distortions inherent in panoramic images.<n>This enables robust 3D-aware feature representations and real-time rendering capabilities.
  arXiv  Detail & Related papers  (2024-12-09T06:58:31Z)
• FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenes [50.534213038479926]
  FreeSplat is capable of reconstructing geometrically consistent 3D scenes from long sequence input towards free-view synthesis. We propose a simple but effective free-view training strategy that ensures robust view synthesis across broader view range regardless of the number of views.
  arXiv  Detail & Related papers  (2024-05-28T08:40:14Z)
• SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
  We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene. SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
  arXiv  Detail & Related papers  (2024-04-29T23:26:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.