论文标题
SEEXERF：带有辐射场的自我监管的单眼3D场景重建
SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields
论文作者
论文摘要
文献中广泛介绍了来自单个2D图像的3D重建，但依赖于训练时的深度监督，从而限制了其适用性。为了放大对深度的依赖性，我们提出了Scenerf，这是一种仅使用带姿势的图像序列进行训练的自我监督的单眼重建方法。随着神经辐射场（NERF）最近进展的推动，我们以明确的深度优化和一种新颖的概率抽样策略来优化辐射场，以有效处理大型场景。在推断时，单个输入图像足以使新的深度视图融合在一起以获得3D场景重建。彻底的实验表明，我们在室内束和室外semantickitti上胜过新的深度综合和场景重建的所有基准。代码可在https://astra-vision.github.io/scenerf上找到。
3D reconstruction from a single 2D image was extensively covered in the literature but relies on depth supervision at training time, which limits its applicability. To relax the dependence to depth we propose SceneRF, a self-supervised monocular scene reconstruction method using only posed image sequences for training. Fueled by the recent progress in neural radiance fields (NeRF) we optimize a radiance field though with explicit depth optimization and a novel probabilistic sampling strategy to efficiently handle large scenes. At inference, a single input image suffices to hallucinate novel depth views which are fused together to obtain 3D scene reconstruction. Thorough experiments demonstrate that we outperform all baselines for novel depth views synthesis and scene reconstruction, on indoor BundleFusion and outdoor SemanticKITTI. Code is available at https://astra-vision.github.io/SceneRF .
