  We propose a method for in-hand 3D scanning of an unknown object with a
monocular camera. Our method relies on a neural implicit surface representation
that captures both the geometry and the appearance of the object, however, by
contrast with most NeRF-based methods, we do not assume that the camera-object
relative poses are known. Instead, we simultaneously optimize both the object
shape and the pose trajectory. As direct optimization over all shape and pose
parameters is prone to fail without coarse-level initialization, we propose an
incremental approach that starts by splitting the sequence into carefully
selected overlapping segments within which the optimization is likely to
succeed. We reconstruct the object shape and track its poses independently
within each segment, then merge all the segments before performing a global
optimization. We show that our method is able to reconstruct the shape and
color of both textured and challenging texture-less objects, outperforms
classical methods that rely only on appearance features, and that its
performance is close to recent methods that assume known camera poses.
