27 research outputs found
Efficient Structured Prediction with Latent Variables for General Graphical Models
In this paper we propose a unified framework for structured prediction with
latent variables which includes hidden conditional random fields and latent
structured support vector machines as special cases. We describe a local
entropy approximation for this general formulation using duality, and derive an
efficient message passing algorithm that is guaranteed to converge. We
demonstrate its effectiveness in the tasks of image segmentation as well as 3D
indoor scene understanding from single images, showing that our approach is
superior to latent structured support vector machines and hidden conditional
random fields.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Towards Scene Understanding with Detailed 3D Object Representations
Current approaches to semantic image and scene understanding typically employ
rather simple object representations such as 2D or 3D bounding boxes. While
such coarse models are robust and allow for reliable object detection, they
discard much of the information about objects' 3D shape and pose, and thus do
not lend themselves well to higher-level reasoning. Here, we propose to base
scene understanding on a high-resolution object representation. An object class
- in our case cars - is modeled as a deformable 3D wireframe, which enables
fine-grained modeling at the level of individual vertices and faces. We augment
that model to explicitly include vertex-level occlusion, and embed all
instances in a common coordinate frame, in order to infer and exploit
object-object interactions. Specifically, from a single view we jointly
estimate the shapes and poses of multiple objects in a common 3D frame. A
ground plane in that frame is estimated by consensus among different objects,
which significantly stabilizes monocular 3D pose estimation. The fine-grained
model, in conjunction with the explicit 3D scene model, further allows one to
infer part-level occlusions between the modeled objects, as well as occlusions
by other, unmodeled scene elements. To demonstrate the benefits of such
detailed object class models in the context of scene understanding we
systematically evaluate our approach on the challenging KITTI street scene
dataset. The experiments show that the model's ability to utilize image
evidence at the level of individual parts improves monocular 3D pose estimation
w.r.t. both location and (continuous) viewpoint.Comment: International Journal of Computer Vision (appeared online on 4
November 2014). Online version:
http://link.springer.com/article/10.1007/s11263-014-0780-