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Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting

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arxiv 2404.19758 v1 pith:XHSDK5O3 submitted 2024-04-30 cs.CV

Paul Engstler , Andrea Vedaldi , Iro Laina , Christian Rupprecht This is my paper · ORCID
classification cs.CV
keywords scenedepthexistinggeneratedgenerationgeometryimagesintroduce
verification ladder T0 review T1 audit T2 compute T3 formal
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3D scene generation has quickly become a challenging new research direction, fueled by consistent improvements of 2D generative diffusion models. Most prior work in this area generates scenes by iteratively stitching newly generated frames with existing geometry. These works often depend on pre-trained monocular depth estimators to lift the generated images into 3D, fusing them with the existing scene representation. These approaches are then often evaluated via a text metric, measuring the similarity between the generated images and a given text prompt. In this work, we make two fundamental contributions to the field of 3D scene generation. First, we note that lifting images to 3D with a monocular depth estimation model is suboptimal as it ignores the geometry of the existing scene. We thus introduce a novel depth completion model, trained via teacher distillation and self-training to learn the 3D fusion process, resulting in improved geometric coherence of the scene. Second, we introduce a new benchmarking scheme for scene generation methods that is based on ground truth geometry, and thus measures the quality of the structure of the scene.

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Cited by 5 Pith papers

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

  1. BloomScene: Lightweight Structured 3D Gaussian Splatting for Crossmodal Scene Generation

    cs.CV 2025-01 conditional novelty 6.0 of 10

    BloomScene generates 3D scenes from text or images by combining progressive point cloud construction, depth-prior regularization, and hash-grid compression, cutting storage about 5.8x versus LucidDreamer.

  2. Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach

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    A self-supervised segmentation model detects synthetic fingerprint mosaicking artifacts and a new score quantifies their severity, but real-artifact validation is missing.

  3. You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale

    cs.CV 2024-12 reject novelty 6.0 of 10

    See3D proposes a pose-free visual condition for multi-view diffusion trained on web videos, claiming SOTA single- and sparse-view 3D generation, but the evaluation protocol leaks ground-truth information and mixes ben...

  4. Amodal Depth Anything: Amodal Depth Estimation in the Wild

    cs.CV 2024-12 conditional novelty 6.0 of 10

    The paper introduces ADIW, a 564K-image pseudo-labeled dataset for relative amodal depth, and two fine-tuned models (Amodal-DAV2 and Amodal-DepthFM) that predict occluded-object depth from an image, observed depth, an...

  5. From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence

    cs.RO 2026-07 conditional novelty 4.0 of 10

    Physical intelligence needs an embodied brain that reasons over interventions and emits capability requests, grounded by a physical harness and shared experience contracts rather than direct actuator policies.

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