  We present an efficient and automatic approach for accurate reconstruction of
instances of big 3D objects from multiple, unorganized and unstructured point
clouds, in presence of dynamic clutter and occlusions. In contrast to
conventional scanning, where the background is assumed to be rather static, we
aim at handling dynamic clutter where background drastically changes during the
object scanning. Currently, it is tedious to solve this with available methods
unless the object of interest is first segmented out from the rest of the
scene. We address the problem by assuming the availability of a prior CAD
model, roughly resembling the object to be reconstructed. This assumption
almost always holds in applications such as industrial inspection or reverse
engineering. With aid of this prior acting as a proxy, we propose a fully
enhanced pipeline, capable of automatically detecting and segmenting the object
of interest from scenes and creating a pose graph, online, with linear
complexity. This allows initial scan alignment to the CAD model space, which is
then refined without the CAD constraint to fully recover a high fidelity 3D
reconstruction, accurate up to the sensor noise level. We also contribute a
novel object detection method, local implicit shape models (LISM) and give a
fast verification scheme. We evaluate our method on multiple datasets,
demonstrating the ability to accurately reconstruct objects from small sizes up
to $125m^3$.
