Derinlik ve segmentasyon kullanarak tek görüntülerden bilinen nesnelerin hacim tahmini

This thesis delves into the challenging task of estimating the volume of known objects from a single-view image perspective. In this thesis, we proposed a new model for estimating the volume of objects from just one image. Leveraging state-of-the-art deep learning methodologies, the image undergoes a comprehensive analysis involving depth and segmentation networks. The depth network is responsible for estimating the object's depth map, while the segmentation network determines the pose of the objects within the image. Combining these outputs with the intrinsic data of the camera results in the creation of a detailed point cloud. This point cloud serves as the foundational data source for precise volume estimation, contributing to advancements in the field of computer vision. The experiments on publicly available datasets show that our method outperforms the other methods and achieves the state-of-the-art performance.