  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.
