Neural implicit representations have emerged as a powerful paradigm for 3D reconstruction. However, despite their success, existing methods fail to capture fine geometric details and thin structures, especially in scenarios where only sparse RGB views of the objects of interest are available. We hypothesize that current methods for learning neural implicit representations from RGB or RGBD images produce 3D surfaces with missing parts and details because they only rely on 0-order differential properties, i.e. the 3D surface points and their projections, as supervisory signals. Such properties, however, do not capture the local 3D geometry around the points and also ignore the interactions between points. This paper demonstrates that training neural representations with first-order differential properties, i.e. surface normals, leads to highly accurate 3D surface reconstruction even in situations where only as few as two RGB (front and back) images are available. Given multiview RGB images of an object of interest, we first compute the approximate surface normals in the image space using the gradient of the depth maps produced using an off-the-shelf monocular depth estimator such as Depth Anything model. An implicit surface regressor is then trained using a loss function that enforces the first-order differential properties of the regressed surface to match those estimated from Depth Anything. Our extensive experiments on a wide range of real and synthetic datasets show that the proposed method achieves an unprecedented level of reconstruction accuracy even when using as few as two RGB views. The detailed ablation study also demonstrates that normal-based supervision plays a key role in this significant improvement in performance, enabling the 3D reconstruction of intricate geometric details and thin structures that were previously challenging to capture.

通过使用一阶微分特性(即，表面法线)，本文展示了训练神经表示形成高准确度的3D表面重建，即使只有两个RGB视图可用，极大地改善了以前难以捕捉的复杂几何细节和薄结构。

基于法向引导的高保真三维曲面重建的神经隐式函数