Summary: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1
A Self-Calibrating Method for Photogeometric Acquisition of 3D Objects
Daniel G. Aliaga Yi Xu
Abstract-- We present a self-calibrating photogeometric method using only off-the-shelf hardware that enables quickly and
robustly obtaining multi-million point-sampled and colored models of real-world objects. Some previous efforts use a priori
calibrated systems to separately acquire geometric and photometric information. Our key enabling observation is that a digital
projector can be simultaneously used as either an active light source or as a virtual camera (as opposed to a digital camera
which cannot be used for both). We present our self-calibrating and multi-viewpoint 3D acquisition method, based on structured-
light, which simultaneously obtains mutually registered surface position and surface normal information and produces a single
high-quality model. Acquisition processing freely alternates between using a geometric setup and using a photometric setup with
the same hardware configuration. Further, our approach generates reconstructions at the resolution of the camera and not only
the projector. We show the results of capturing several high-quality models of real-world objects.
Index Terms-- Three Digitization and Image Capture, Scene Analysis, Geometric Modeling.
WE present a new self-calibrating method for acquiring
highly-detailed models for 3D objects. Our method uses
off-the-shelf uncalibrated digital projectors and cameras
and enables a computational trade-off from coarse and
fast acquisitions to highly-detailed and optimized models.