RI: Small: Object Detection, Pose Estimation, and Semantic Segmentation Using 3D Wireframe Models

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Humans can recognize a wide variety of object categories in an image, even in the midst of occlusion and clutter, and in spite of significant variability in the objects' shape, size, appearance, and pose. While automatic object recognition systems in computer vision have seen great progress over the past decade, state-of-the-art methods rely primarily on 2D representations of object categories, which cannot capture large variations in the objects' shape and pose. This project develops new 3D representations of objects that are general enough to describe the shape of a wide variety of object categories, yet simple enough so that they can be learned efficiently from a collection of 2D images, and used for detecting and segmenting objects in new images. This project can impact many research areas, including image search, autonomous navigation, medical diagnostic tools, surveillance and robotics.This project develops a new class of 3D wireframe models for view-invariant object detection, fine-grained pose estimation, and semantic segmentation. A wireframe model is a sparse collection of 3D points, edges and surface normals defined only at a few points on the boundaries of the 3D object. The project develops methods for learning wireframe models from 2D images of multiple object categories, methods for learning deformable wireframe models that capture intra-class shape variations across object categories, methods for integrating appearance information into wireframe models, and methods for semantic segmentation based on wireframe models.