TY - JOUR
T1 - Dip Transform for 3D Shape Reconstruction
AU - Aberman, Kfir
AU - Katzir, Oren
AU - Zhou, Qiang
AU - Luo, Zegang
AU - Sharf, Andrei
AU - Greif, Chen
AU - Chen, Baoquan
AU - Cohen-Or, Daniel
N1 - Funding Information:
We thank the anonymous reviewers for their helpful comments. Œis project was supported in part by the Joint NSFC-ISF Research Program 61561146397, jointly funded by the National Natural Science Foundation of China and the Israel Science Foundation (No. 61561146397), the National Basic Research grant (973) (No. 2015CB352501) and the NSERC of Canada grant 261539.
Publisher Copyright:
© 2017 ACM.
PY - 2017/7
Y1 - 2017/7
N2 - The paper presents a novel three-dimensional shape acquisition and reconstruction method based on the well-known Archimedes equality between fluid displacement and the submerged volume. By repeatedly dipping a shape in liquid in different orientations and measuring its volume displacement, we generate the dip transform: a novel volumetric shape representation that characterizes the object's surface. The key feature of our method is that it employs fluid displacements as the shape sensor. Unlike optical sensors, the liquid has no line-of-sight requirements, it penetrates cavities and hidden parts of the object, as well as transparent and glossy materials, thus bypassing all visibility and optical limitations of conventional scanning devices. Our new scanning approach is implemented using a dipping robot arm and a bath of water, via which it measures the water elevation. We show results of reconstructing complex 3D shapes and evaluate the quality of the reconstruction with respect to the number of dips.
AB - The paper presents a novel three-dimensional shape acquisition and reconstruction method based on the well-known Archimedes equality between fluid displacement and the submerged volume. By repeatedly dipping a shape in liquid in different orientations and measuring its volume displacement, we generate the dip transform: a novel volumetric shape representation that characterizes the object's surface. The key feature of our method is that it employs fluid displacements as the shape sensor. Unlike optical sensors, the liquid has no line-of-sight requirements, it penetrates cavities and hidden parts of the object, as well as transparent and glossy materials, thus bypassing all visibility and optical limitations of conventional scanning devices. Our new scanning approach is implemented using a dipping robot arm and a bath of water, via which it measures the water elevation. We show results of reconstructing complex 3D shapes and evaluate the quality of the reconstruction with respect to the number of dips.
KW - shape reconstruction
KW - volume
KW - data acquisition
UR - http://www.scopus.com/inward/record.url?scp=85030762299&partnerID=8YFLogxK
U2 - 10.1145/3072959.3073693
DO - 10.1145/3072959.3073693
M3 - Conference article
AN - SCOPUS:85030762299
VL - 36
JO - ACM Transactions on Graphics
JF - ACM Transactions on Graphics
SN - 0730-0301
IS - 4
M1 - 79
T2 - ACM SIGGRAPH 2017
Y2 - 30 July 2017 through 3 August 2017
ER -