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Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image

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arxiv 2406.04343 v2 pith:SFBD33OQ submitted 2024-06-06 cs.CV

Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image

Stanislaw Szymanowicz , Eldar Insafutdinov , Chuanxia Zheng , Dylan Campbell , Jo\~ao F. Henriques , Christian Rupprecht , Andrea Vedaldi This is my paper
classification cs.CV
keywords flash3dreconstructionsinglewhenachievesdepthefficientfeed-forward
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We propose Flash3D, a method for scene reconstruction and novel view synthesis from a single image which is both very generalisable and efficient. For generalisability, we start from a "foundation" model for monocular depth estimation and extend it to a full 3D shape and appearance reconstructor. For efficiency, we base this extension on feed-forward Gaussian Splatting. Specifically, we predict a first layer of 3D Gaussians at the predicted depth, and then add additional layers of Gaussians that are offset in space, allowing the model to complete the reconstruction behind occlusions and truncations. Flash3D is very efficient, trainable on a single GPU in a day, and thus accessible to most researchers. It achieves state-of-the-art results when trained and tested on RealEstate10k. When transferred to unseen datasets like NYU it outperforms competitors by a large margin. More impressively, when transferred to KITTI, Flash3D achieves better PSNR than methods trained specifically on that dataset. In some instances, it even outperforms recent methods that use multiple views as input. Code, models, demo, and more results are available at https://www.robots.ox.ac.uk/~vgg/research/flash3d/.

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Cited by 5 Pith papers

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    UniSHARP performs universal sharp monocular view synthesis by implicit alignment of diverse camera images in a unified omnidirectional latent space using ray-arranged Gaussian primitives and UniK3D-inspired feature decoding.

  2. Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective

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    The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temp...

  3. GeoWorld: Providing Full-frame Geometry Features to Facilitate 3D Scene Generation

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    GeoWorld improves image-to-3D scene generation by conditioning a video-diffusion model on full-frame geometry features extracted by a multi-view geometry model, yielding higher PSNR/SSIM/LPIPS than prior methods.

  4. Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs

    cs.CV 2024-08 unverdicted novelty 5.0

    Splatt3R is a feed-forward network that predicts 3D Gaussian splats directly from uncalibrated stereo image pairs by extending MASt3R with appearance attributes and a two-stage training procedure.

  5. A Survey on 3D Gaussian Splatting

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    A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.