Publications

In this survey, we provide a detailed analysis of datasets in the highly investigated object recognition areas. More than 160 datasets have been scrutinized through statistics and descriptions. Additionally, we present an overview of the prominent object recognition benchmarks and competitions, along with a description of the metrics widely adopted for evaluation purposes in the computer vision community. All introduced datasets and challenges .

In this paper, we propose 3DCADFusion, an end-to-end object reconstruction pipeline. Our system is capable of producing dense high-fidelity reconstructions of objects by simultaneously tracking and reconstructing their incrementally improving 3D geometry in a sequence of frames captured by a depth-enabled camera setup. In this process, no external information about the object motion is required as long as the camera is stationary. This is accomplished by removing irrelevant scene elements such as the background or the operator hands holding the object to track and fuse its shape over time only using pixels associated with the object mask. 

This thesis presents an end-to-end object reconstruction pipeline that includes image data acquisition, processing, and visualization. The proposed system produces high-fidelity 3D models by tracking a handheld object in a sequence of RGB-D frames. In this process, a semantic segmentation network is used to remove the operator hand from the frame to create an object mask. The mask is then used to track the pose of the object over time. A truncated signed distance function representation is used to fuse the aligned frames into a global model.