|
3D
Modeling |
SCAPE: Shape Completion and Animation of PeopleD. Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers, J. Davis |
A data-driven approach for
building a human shape model which spans variation in both subject
shape and pose from 3D scans. The model is useful for a variety of
animation and shape completion tasks. We can synthesize complete 3D
surfaces for a subject using the output of a marker-based motion
capture system. We can also use our model to complete a partial scans
of different people in different poses. [SIGGRAPH 2005] [Go to Project Page] |
The Correlated Correspondence Algorithm for Surface RegistrationD. Anguelov, P. Srinivasan, D. Koller, S. Thrun, H.-C. Pang, J. Davis |
An algorithm for registering 3D
surface scans of an object undergoing significant deformations.
The algorithm registers two meshes by optimizing a joint probabilistic
model over all point-to-point correspondences between them, which
attempts to capture local geometry and preserve geodesic distances. The
algorithm does not
need markers, nor does it assume
prior knowledge about object shape, the dynamics of its deformation, or
scan alignment (although such knowledge can be incorporated if it is
available). [NIPS 2004] [Go to Project Page] |
Recovering Articulated Object Models from 3D Range DataD. Anguelov, D. Koller, H.-C. Pang, P. Srinivasan, S. Thrun |
We describe an algorithm whose
input is a set of meshes corresponding to different configurations of
an articulated object. The algorithm automatically recovers a decomposition of the object into approximately rigid parts, the location of the parts in the different object instances, and the articulated object skeleton linking the parts. It assumes the correspondences between the scans are known (we use the Correlated Correspondence algorithm above to recover them). [UAI 2004] [Go to Project Page] |
3D Scene
Segmentation |
Discriminative Learning of Markov Random Fields for Segmentation of 3D Range DataD. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta, G. Heitz, A. Ng |
We address the problem of
segmenting 3D scan data into objects or object classes. Our
segmentation framework is based on a subclass of Markov Random Fields
(MRFs) which support efficient graph-cut inference. The MRF
models incorporate a large set of diverse features and enforce
the preference that adjacent scan points have the same classification
label. We use a recently proposed maximum-margin framework to
discriminatively train the model from a set of labeled scans; as a
result we automatically learn the relative importance of the features
for the segmentation task. [CVPR 2005] [Go to Project Page] |