Filling the Unseen: Scene Extrapolation via 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2609.13262v1
• Date: Sun, 06 Sep 2026 19:02:27 GMT
• Title: Filling the Unseen: Scene Extrapolation via 3D Gaussian Splatting
• Abstract summary: 3D Gaussian Splatting reconstruction within training view distribution degrades on out-of-distribution novel views, exhibiting holes in unobserved regions and artifacts in observable areas.<n>Recent works formulate this task as extrapolation and conditioning and try to address it with generative models, but remain limited in extrapolation scale and quality.<n>We propose a holistic framework for extrapolation and conditioning, with an independent camera view detection mechanism to enable parallel conflict-free extrapolation.
• Score: 10.582831709592789
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting achieves photorealistic reconstruction within training view distribution, yet it degrades on out-of-distribution novel views, exhibiting holes in unobserved regions and artifacts in observable areas. Recent works formulate this task as extrapolation and interpolation and try to address it with generative models, but remain limited in extrapolation scale and quality. They repeat a generate-reconstruct-shift cycle to progressively build a scene, which introduces accumulated errors with every step conditioning on previous outcomes. In this work, we propose a holistic framework for extrapolation and interpolation. We devise an independent camera view detection mechanism to enable parallel conflict-free extrapolation, circumventing the reliance on the aforementioned error-prone cycle. Building upon this, we design a hierarchical pipeline that extrapolates independent and dependent camera views separately. Additionally, previous methods overlook inconsistency between generated and original images, resulting in compromising well-reconstructed areas. We propose a plug-and-play Quality-Aware Mask (QA-Mask) module, enabling selective utilization of generated data. By calibrating learning weights with pixel-wise rendering quality, it prevents generation-induced degradations on well-constructed areas. Extensive experiments demonstrate the superior performance of our framework, with QA-Mask generalizing to multiple generative reconstruction models.

Related papers

• GenRec: Knowing Where to Reconstruct and Where to Generate [106.48347271421765]
  GenRec is a flow matching model that builds the reconstruction--generation split directly into its architecture, supervision, and gradient flow.<n>It attains the best reconstruction fidelity in observed regions while also surpassing purely generative baselines on perceptual quality in unobserved ones.
  arXiv  Detail & Related papers  (2026-08-18T14:31:19Z)
• Visual Geometry Transformer in the Wild: Distractor-Free 3D Reconstruction [77.75911117889915]
  Current end-to-end multi-view 3D reconstruction methods achieve impressive results, but rely on a restrictive static assumption.<n>This reliance on idealized inputs causes even the most advanced methods to fail in real-world settings.<n>We propose Visual Geometry Transformer in the Wild, an end-to-end framework for robust reconstruction from inconsistent views.
  arXiv  Detail & Related papers  (2026-06-22T02:52:22Z)
• Sparse-to-Complete: From Sparse Image Captures to Complete 3D Scenes [32.47917212506622]
  S2C-3D is a novel sparse-view 3D reconstruction framework for high-fidelity and complete scene reconstruction from as few as six to eight images.<n>Our framework features three components: a specialized diffusion model for scene-specific image restoration, a training-free view-consistency conditioned sampling process, and a camera trajectory planning scheme.
  arXiv  Detail & Related papers  (2026-05-07T04:41:30Z)
• 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)
• Complete Gaussian Splats from a Single Image with Denoising Diffusion Models [24.95877981068105]
  We propose a latent diffusion model to reconstruct a complete 3D scene with Gaussian splats from only a single image during inference.<n>Our method generates faithful reconstructions and diverse samples with the ability to complete the occluded surfaces for high-quality 360-degree renderings.
  arXiv  Detail & Related papers  (2025-08-29T11:55:47Z)
• DeOcc-1-to-3: 3D De-Occlusion from a Single Image via Self-Supervised Multi-View Diffusion [50.90541069907167]
  We propose DeOcc-1-to-3, an end-to-end framework for occlusion-aware multi-view generation.<n>Our self-supervised training pipeline leverages occluded-unoccluded image pairs and pseudo-ground-truth views to teach the model structure-aware completion and view consistency.
  arXiv  Detail & Related papers  (2025-06-26T17:58:26Z)
• Uncertainty-Aware Diffusion Guided Refinement of 3D Scenes [39.44244142263019]
  Reconstructing 3D scenes from a single image is a fundamentally ill-posed task due to the severely under-constrained nature of the problem.<n>In this work, we address these inherent limitations in existing single image-to-3D scene feedforward networks.<n>To alleviate the poor performance due to insufficient information beyond the input image's view, we leverage a strong generative prior in the form of a pre-trained latent video diffusion model.
  arXiv  Detail & Related papers  (2025-03-19T23:14:27Z)
• FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction [69.63414788486578]
  FreeSplatter is a scalable feed-forward framework that generates high-quality 3D Gaussians from uncalibrated sparse-view images.<n>Our approach employs a streamlined transformer architecture where self-attention blocks facilitate information exchange.<n>We develop two specialized variants--for object-centric and scene-level reconstruction--trained on comprehensive datasets.
  arXiv  Detail & Related papers  (2024-12-12T18:52:53Z)
• Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors [8.440211083233528]
  We introduce a generative approach for pose-free (without camera parameters) reconstruction of 360 scenes from a sparse set of 2D images.<n>We propose an image-to-image generative model designed to inpaint missing details and remove artifacts in novel view renders and depth maps of a 3D scene.
  arXiv  Detail & Related papers  (2024-11-24T19:34:58Z)
• No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images [100.80376573969045]
  NoPoSplat is a feed-forward model capable of reconstructing 3D scenes parameterized by 3D Gaussians from multi-view images. Our model achieves real-time 3D Gaussian reconstruction during inference. This work makes significant advances in pose-free generalizable 3D reconstruction and demonstrates its applicability to real-world scenarios.
  arXiv  Detail & Related papers  (2024-10-31T17:58:22Z)
• Cameras as Rays: Pose Estimation via Ray Diffusion [54.098613859015856]
  Estimating camera poses is a fundamental task for 3D reconstruction and remains challenging given sparsely sampled views. We propose a distributed representation of camera pose that treats a camera as a bundle of rays. Our proposed methods, both regression- and diffusion-based, demonstrate state-of-the-art performance on camera pose estimation on CO3D.
  arXiv  Detail & Related papers  (2024-02-22T18:59:56Z)

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.