11.5.3 Generation from Sparse Data

Chapter Contents (Back)
Symmetry. Generalized Cylinder Generation. Sparse Data.

Sato, H., and Binford, T.O.,
Finding and Recovering SHGC Objects in an Edge Image,
CVGIP(57), No. 3, May 1993, pp. 346-358.
DOI Link BibRef 9305
BUILDER-I: A System for the Extraction of SHGC Objects in an Edge Image,
DARPA92(779-791). Describes a method to extract theSHGC from a noisy edge image. BibRef

Sato, H., and Binford, T.O.,
On Finding the Ends of SHGCs in an Edge Image,
IEEE DOI BibRef 9200
Earlier: DARPA92(379-388). BibRef

Rao, K.G.[Kashipati G.], and Nevatia, R.,
Describing and Segmenting Scenes from Imperfect and Incomplete Data,
CVGIP(57), No. 1, January 1993, pp. 1-23.
DOI Link BibRef 9301 USC Computer Vision
PDF File. BibRef
Shape Descriptions from Imperfect and Incomplete Data,
ICPR90(I: 125-129).
Descriptions of Complex Objects from Incomplete and Imperfect Data,
DARPA89(399-414). This extends
See also Computing Volume Descriptions from Sparse 3-D Data. The descriptions are based on GCs with segmentation into separate parts. BibRef

Rao, K.G., and Nevatia, R.,
Computing Volume Descriptions from Sparse 3-D Data,
IJCV(2), No. 1, June 1988, pp. 33-50.
Springer DOI BibRef 8806 USC Computer Vision BibRef
And: ASR-II90Chapter 2. BibRef
From Sparse 3-D Data Directly to Volumetric Shape Descriptions,
DARPA87(360-369). BibRef
Generalized Cone Descriptions from Sparse 3-D Data,
CVPR86(256-263). BibRef
And: DARPA85(497-505). Generation of generalized cylinder representations using sparse data based on symmetries of boundary elements. BibRef

Rao, K., Nevatia, R., Medioni, G.,
Issues in Shape Description and an Approach for Working with Sparse Data,
SRMSF87(168-177). BibRef 8700 USC Computer Vision BibRef

Rao, K.,
Shape Description from Sparse and Imperfect Data,
Ph.D.December 1988. BibRef 8812 USC_IRIS-250. BibRef USC Computer Vision BibRef

Rao, K., Medioni, G., Liu, H., Bekey, G.A.,
Robot Hand-Eye Coordination: Shape Description and Grasping,
CRA88(407-411). BibRef 8800 USC Computer Vision BibRef

Shi, Y.F.[Yi-Fei], Xu, X.[Xin], Xi, J.H.[Jun-Hua], Hu, X.C.[Xiao-Chang], Hu, D.[Dewen], Xu, K.[Kai],
Learning to Detect 3D Symmetry From Single-View RGB-D Images With Weak Supervision,
PAMI(45), No. 4, April 2023, pp. 4882-4896.
Shape, Annotations, Training, Solid modeling, Transformers, Neural networks, Symmetry detection, weakly-supervised learning, deep neural networks BibRef

Wang, D.[Dong], Zhang, Z.[Zhao], Zhao, Z.W.[Zi-Wei], Liu, Y.H.[Yu-Hang], Chen, Y.H.[Yi-Hong], Wang, L.W.[Li-Wei],
PointScatter: Point Set Representation for Tubular Structure Extraction,
Springer DOI 2211

Schmidt, T.[Thorsten], Keuper, M.[Margret], Pasternak, T.[Taras], Palme, K.[Klaus], Ronneberger, O.[Olaf],
Modeling of Sparsely Sampled Tubular Surfaces Using Coupled Curves,
Springer DOI 1209

Chaperon, T., Goulette, F.[François],
Extracting Cylinders in Full 3-D Data Using a Random Sampling Method and the Gaussian Image,
VMV01(xx-yy). 0209

Chapter on 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing continues in
Generalized Cylinder Generation from Intensity Data .

Last update:Jun 17, 2024 at 21:38:11