In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work is based on a key observation -- depth estimation in recent approaches is surprisingly inadequate given the fact that depth is essential to camera 3D detection. Our BEVDepth resolves this by leveraging explicit depth supervision. A camera-awareness depth estimation module is also introduced to facilitate the depth predicting capability. Besides, we design a novel Depth Refinement Module to counter the side effects carried by imprecise feature unprojection. Aided by customized Efficient Voxel Pooling and multi-frame mechanism, BEVDepth achieves the new state-of-the-art 60.9% NDS on the challenging nuScenes test set while maintaining high efficiency. For the first time, the NDS score of a camera model reaches 60%. Codes have been released.

Scene flow prediction is a challenging task that aims at jointly estimating the 3D structure and 3D motion of dynamic scenes. The previous methods concentrate more on point-wise estimation instead of considering the correspondence between objects as well as lacking the sensation of high-level semantic knowledge. In this paper, we propose a concise yet effective method for scene flow prediction. The key idea is to extend the view of all points for computing point cloud features into object-level, thus simultaneously modeling the relationships of the object-level and point-level via an improved transformer. In addition, we introduce a novel unsupervised loss called segmentation-aware loss, which can model semanticaware details to help predict scene flow more accurately and robustly. Since this loss can be trained without any ground truth, it can be used in both supervised training and self-supervised training. Experiments on both supervised training and self-supervised training demonstrate the effectiveness of our method. On supervised training, 3.8%, 22.58%, 10.90% and 21.82 % accuracy boosts than FLOT [23] can be observed on FT3Ds, KITTIs, FT3Do and KITTIo datasets. On self-supervised scheme, 48.23% and 48.96% accuracy boost than PointPWC-Net [40] can be observed on KITTIo and KITTIs datasets.

Feature distillation, a primary method in knowledge distillation, always leads to significant accuracy improvements. Most existing methods distill features in the teacher network through a manually designed transformation. In this paper, we propose a novel distillation method named task-oriented feature distillation (TOFD) where the transformation is convolutional layers that are trained in a data-driven manner by task loss. As a result, the task-oriented information in the features can be captured and distilled to students. Moreover, an orthogonal loss is applied to the feature resizing layer in TOFD to improve the performance of knowledge distillation. Experiments show that TOFD outperforms other distillation methods by a large margin on both image classification and 3D classification tasks. Codes have been released in Github.