We present a unified occlusion model for object instance detection under arbitrary viewpoint. Whereas previous approaches primarily modeled local coherency of occlusions or attempted to learn the structure of occlusions from data, we propose to explicitly model occlusions by reasoning about 3D interactions of objects. Our approach accurately represents occlusions under arbitrary viewpoint without requiring additional training data, which can often be difficult to obtain. We validate our model by extending the state-of-the-art LINE2D method for object instance detection and demonstrate significant improvement in recognizing texture-less objects under severe occlusions.
We present a framework that retains ambiguity in feature matching to increase the performance of 3D object recognition systems. Whereas previous systems removed ambiguous correspondences during matching, we show that ambiguity should be resolved during hypothesis testing and not at the matching phase. To preserve ambiguity during matching, we vector quantize and match model features in a hierarchical manner. This matching technique allows our system to be more robust to the distribution of model descriptors in feature space. We also show that we can address recognition under arbitrary viewpoint by using our framework to facilitate matching of additional features extracted from affine transformed model images. The evaluation of our algorithms in 3D object recognition is demonstrated on a difficult dataset of 620 images.
We present a unified occlusion model for object instance detection under arbitrary viewpoint. Whereas previous approaches primarily modeled local coherency of occlusions or attempted to learn the structure of occlusions from data, we propose to explicitly model occlusions by reasoning about 3D interactions of objects. Our approach accurately represents occlusions under arbitrary viewpoint without requiring additional training data, which can often be difficult to obtain. We validate our model by extending the state-of-the-art LINE2D method for object instance detection and demonstrate significant improvement in recognizing texture-less objects under severe occlusions.
We present a new approach for recognizing the make and model of a car from a single image. While most previous methods are restricted to fixed or limited viewpoints, our system is able to verify a car's make and model from an arbitrary view. Our model consists of 3D space curves obtained by backprojecting image curves onto silhouettebased visual hulls and then refining them using three-view curve matching. These 3D curves are then matched to 2D image curves using a 3D view-based alignment technique. We present two different methods for estimating the pose of a car, which we then use to initialize the 3D curve matching. Our approach is able to verify the exact make and model of a car over a wide range of viewpoints in cluttered scenes.
Abstract-Occlusions are common in real world scenes and are a major obstacle to robust object detection. In this paper, we present a method to coherently reason about occlusions on many types of detectors. Previous approaches primarily enforced local coherency or learned the occlusion structure from data. However, local coherency ignores the occlusion structure in real world scenes and learning from data requires tediously labeling many examples of occlusions for every view of every object. Other approaches require binary classifications of matching scores. We address these limitations by formulating occlusion reasoning as an efficient search over occluding blocks which best explain a probabilistic matching pattern. Our method demonstrates significant improvement in estimating the mask of the occluding region and improves object instance detection on a challenging dataset of objects under severe occlusions.
We present a novel framework for shape-based template matching in images. While previous approaches required brittle contour extraction, considered only local information, or used coarse statistics, we propose to match the shape explicitly on low-level gradients by formulating the problem as traversing paths in a gradient network. We evaluate our algorithm on a challenging dataset of objects in cluttered environments and demonstrate significant improvement over state-of-the-art methods for shape matching and object detection.
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