Pith. sign in

REVIEW 3 cited by

LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2408.13252 v2 pith:7ZF7WLBH submitted 2024-08-23 cs.CV

classification cs.CV
keywords scenepanoramagenerationlayerpano3dhierarchiespanoramiccomplexconsistency
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

3D immersive scene generation is a challenging yet critical task in computer vision and graphics. A desired virtual 3D scene should 1) exhibit omnidirectional view consistency, and 2) allow for free exploration in complex scene hierarchies. Existing methods either rely on successive scene expansion via inpainting or employ panorama representation to represent large FOV scene environments. However, the generated scene suffers from semantic drift during expansion and is unable to handle occlusion among scene hierarchies. To tackle these challenges, we introduce Layerpano3D, a novel framework for full-view, explorable panoramic 3D scene generation from a single text prompt. Our key insight is to decompose a reference 2D panorama into multiple layers at different depth levels, where each layer reveals the unseen space from the reference views via diffusion prior. Layerpano3D comprises multiple dedicated designs: 1) We introduce a new panorama dataset Upright360, comprising 9k high-quality and upright panorama images, and finetune the advanced Flux model on Upright360 for high-quality, upright and consistent panorama generation. 2) We pioneer the Layered 3D Panorama as underlying representation to manage complex scene hierarchies and lift it into 3D Gaussians to splat detailed 360-degree omnidirectional scenes with unconstrained viewing paths. Extensive experiments demonstrate that our framework generates state-of-the-art 3D panoramic scene in both full view consistency and immersive exploratory experience. We believe that Layerpano3D holds promise for advancing 3D panoramic scene creation with numerous applications.

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Look Beyond: Two-Stage Scene View Generation via Panorama and Video Diffusion

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Single-image novel view synthesis is decomposed into panorama outpainting plus keyframe-conditioned video diffusion, producing loop-consistent scene tours.

  2. DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via Diffusion Transformers

    cs.CV 2025-05 conditional novelty 6.0 of 10

    DiffDecompose recovers foreground and background layers from alpha-composited images using in-context diffusion with position encoding cloning, trained and evaluated on a new six-task synthetic dataset.

  3. HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A staged pipeline generates layered, mesh-based 3D worlds from text or images by combining panoramic diffusion, semantic layer decomposition, and video-based expansion.

Pith tools