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 this https URL, and more examples can
be found at our website here this https URL

通过结合压缩的自注意力机制、不可微分体素渲染、场景压缩和图像特征投影等方法，我们提出了一种高效、可扩展的基于 Transformer 的编码器 - 渲染器方法，用于从单次拍摄的图像重建 3D 一致的参数化三平面，适用于大规模、无界的户外驾驶场景。