  We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate
and efficient 3D object detection in outdoor point clouds. Our key focus is on
exploiting both soft and hard targets with our formulated constraints to
jointly optimize the model, without introducing extra computation in the
inference. Specifically, SE-SSD contains a pair of teacher and student SSDs, in
which we design an effective IoU-based matching strategy to filter soft targets
from the teacher and formulate a consistency loss to align student predictions
with them. Also, to maximize the distilled knowledge for ensembling the
teacher, we design a new augmentation scheme to produce shape-aware augmented
samples to train the student, aiming to encourage it to infer complete object
shapes. Lastly, to better exploit hard targets, we design an ODIoU loss to
supervise the student with constraints on the predicted box centers and
orientations. Our SE-SSD attains top performance compared with all prior
published works. Also, it attains top precisions for car detection in the KITTI
benchmark (ranked 1st and 2nd on the BEV and 3D leaderboards, respectively)
with an ultra-high inference speed. The code is available at
https://github.com/Vegeta2020/SE-SSD.
