6Img-to-3D: Few-Image Large-Scale Outdoor Driving Scene Reconstruction
<|reference_start|>6Img-to-3D: Few-Image Large-Scale Outdoor Driving Scene Reconstruction: Current 3D reconstruction techniques struggle to infer unbounded scenes from a few images faithfully. Specifically, existing methods have high computational demands, require detailed pose information, and cannot reconstruct occluded regions reliably. We introduce 6Img-to-3D, an efficient, scalable transformer-based encoder-renderer method for single-shot image to 3D reconstruction. Our method outputs a 3D-consistent parameterized triplane from only six outward-facing input images for large-scale, unbounded outdoor driving scenarios. We take a step towards resolving existing shortcomings by combining contracted custom cross- and self-attention mechanisms for triplane parameterization, differentiable volume rendering, scene contraction, and image feature projection. We showcase that six surround-view vehicle images from a single timestamp without global pose information are enough to reconstruct 360$^{\circ}$ scenes during inference time, taking 395 ms. Our method allows, for example, rendering third-person images and birds-eye views. Our code is available at https://github.com/continental/6Img-to-3D, and more examples can be found at our website here https://6Img-to-3D.GitHub.io/.<|reference_end|>
@article{gieruc20246img-to-3d:,
    title={6Img-to-3D: Few-Image Large-Scale Outdoor Driving Scene Reconstruction},
    author={Th'eo Gieruc and Marius K"astingsch"afer and Sebastian Bernhard and
  Mathieu Salzmann},
    journal={arXiv preprint arXiv:2404.12378},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12378},
    primaryClass={cs.CV cs.AI cs.LG}
}