Towards Open-set Camera 3D Object Detection

Traditional camera 3D object detectors are typically trained to recognize a predefined set of known object classes. In real-world scenarios, these detectors may encounter unknown objects outside the training categories and fail to identify them correctly. To address this gap, we present OS-Det3D (Open-set Camera 3D Object Detection), a two-stage training framework enhancing the ability of camera 3D detectors to identify both known and unknown objects. The framework involves our proposed 3D Object Discovery Network (ODN3D), which is specifically trained using geometric cues such as the location and scale of 3D boxes to discover general 3D objects. ODN3D is trained in a class-agnostic manner, and the provided 3D object region proposals inherently come with data noise. To boost accuracy in identifying unknown objects, we introduce a Joint Objectness Selection (JOS) module. JOS selects the pseudo ground truth for unknown objects from the 3D object region proposals of ODN3D by combining the ODN3D objectness and camera feature attention objectness. Experiments on the nuScenes and KITTI datasets demonstrate the effectiveness of our framework in enabling camera 3D detectors to successfully identify unknown objects while also improving their performance on known objects.

OAML: Outlier Aware Metric Learning for OOD Detection Enhancement

Out-of-distribution (OOD) detection methods have been developed to identify objects that a model has not seen during training. The Outlier Exposure (OE) methods use auxiliary datasets to train OOD detectors directly. However, the collection and learning of representative OOD samples may pose challenges. To tackle these issues, we propose the Outlier Aware Metric Learning (OAML) framework. The main idea of our method is to use the k-NN algorithm and Stable Diffusion model to generate outliers for training at the feature level without making any distributional assumptions. To increase feature discrepancies in the semantic space, we develop a mutual information-based contrastive learning approach for learning from OOD data effectively. Both theoretical and empirical results confirm the effectiveness of this contrastive learning technique. Furthermore, we incorporate knowledge distillation into our learning framework to prevent degradation of in-distribution classification accuracy. The combination of contrastive learning and knowledge distillation algorithms significantly enhances the performance of OOD detection. Experimental results across various datasets show that our method significantly outperforms previous OE methods.