3D perception based on the representations learned from multi-camera bird's-eye-view (BEV) is trending as cameras are cost-effective for mass production in autonomous driving industry. However, there exists a distinct performance gap between multi-camera BEV and LiDAR based 3D object detection. One key reason is that LiDAR captures accurate depth and other geometry measurements, while it is notoriously challenging to infer such 3D information from merely image input. In this work, we propose to boost the representation learning of a multi-camera BEV based student detector by training it to imitate the features of a well-trained LiDAR based teacher detector. We propose effective balancing strategy to enforce the student to focus on learning the crucial features from the teacher, and generalize knowledge transfer to multi-scale layers with temporal fusion. We conduct extensive evaluations on multiple representative models of multi-camera BEV. Experiments reveal that our approach renders significant improvement over the student models, leading to the state-of-the-art performance on the popular benchmark nuScenes.

We address the task of action recognition from a sequence of 3D human poses. This is a challenging task firstly because the poses of the same class could have large intra-class variations either caused by inaccurate 3D pose estimation or various performing styles. Also different actions, e.g., walking vs. jogging, may share similar poses which makes the representation not discriminative to differentiate the actions. To solve the problems, we propose a novel representation for 3D poses by a mixture of Discriminative Activated Simplices (DAS). Each DAS consists of a few bases and represent pose data by their convex combinations. The discriminative power of DAS is firstly realized by learning discriminative bases across classes with a block diagonal constraint enforced on the basis coefficient matrix. Secondly, the DAS provides tight characterization of the pose manifolds thus reducing the chance of generating overlapped DAS between similar classes. We justify the power of the model on benchmark datasets and witness consistent performance improvements.