The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods. It does not require prior training, knowledge of the camera parameters, explicit point correspondences or matching features between the image and model. Unlike techniques that estimate a partial 3D pose (as in an overhead view of traffic or machine parts on a conveyor belt), our method estimates the complete 3D pose of the object. It works on a single static image from a given view under varying and unknown lighting conditions. For this purpose we derive a novel illumination-invariant distance measure between the 2D photo and projected 3D model, which is then minimised to find the best pose parameters. Results for vehicle pose detection in real photographs are presented.
2.5D approaches are also viewer centered, where the object is known to occur in a particular view. They differ from the 2D approach as the model stores additional information such as intrinsic image parameters and surface-orientation maps. The work done by Poje and Delp @cite_0 explain the use of intrinsic scene parameters in the form of range (depth) maps and needle (local surface orientation) maps. Shape from shading @cite_23 and photometric stereo @cite_2 are some other examples of the use of the 2.5D approach used for the recognition of industrial parts. A range of techniques for such 2D 2.5D representations are described by Forsythe and Ponce @cite_8 , by posing the object recognition problem as a correspondence problem. These methods obtain a hypothesis based on the correspondences of a few matching points in the image and the model. The hypothesis is validated against the remaining known points.
@cite_0 @cite_8 @cite_23 @cite_2
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This paper deals with recognizing human actions in still images. We make two key contributions. (1) We propose a novel, 2.5D representation of action images that considers both view-independent pose information and rich appearance information. A 2.5D graph of an action image consists of a set of nodes that are key-points of the human body, as well as a set of edges that are spatial relationships between the nodes. Each key-point is represented by view-independent 3D positions and local 2D appearance features. The similarity between two action images can then be measured by matching their corresponding 2.5D graphs. (2) We use an exemplar based action classification approach, where a set of representative images are selected for each action class. The selected images cover large within-action variations and carry discriminative information compared with the other classes. This exemplar based representation of action classes further makes our approach robust to pose variations and occlusions. We test our method on two publicly available datasets and show that it achieves very promising performance. This is a primer on extended Gaussian images. Extended Gaussian images are useful for representing the shapes of surfaces. They can be computed easily from: 1. needle maps obtained using photometric stereo; or 2. depth maps generated by ranging devices or binocular stereo. Importantly, they can also be determined simply from geometric models of the objects. Extended Gaussian images can be of use in at least two of the tasks facing a machine vision system: 1. recognition, and 2. determining the attitude in space of an object. Here, the extended Gaussian image is defined and some of its properties discussed. An elaboration for nonconvex objects is presented and several examples are shown. 3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks. 3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet -- a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.