Jointly Optimizing 3D Model Fitting and Fine-Grained Classification

TLDR: This work proposes to optimize 3D model fitting and fine-grained classification jointly, demonstrating the method outperforms several state-of-the-art approaches and conducting a series of analyses to explore the dependence between fine-Grained classification performance and 3D models.

Abstract: 3D object modeling and fine-grained classification are often treated as separate tasks. We propose to optimize 3D model fitting and fine-grained classification jointly. Detailed 3D object representations encode more information (e.g., precise part locations and viewpoint) than traditional 2D-based approaches, and can therefore improve fine-grained classification performance. Meanwhile, the predicted class label can also improve 3D model fitting accuracy, e.g., by providing more detailed class-specific shape models. We evaluate our method on a new fine-grained 3D car dataset (FG3DCar), demonstrating our method outperforms several state-of-the-art approaches. Furthermore, we also conduct a series of analyses to explore the dependence between fine-grained classification performance and 3D models.