  State-of-the-art lidar-based 3D object detection methods rely on supervised
learning and large labeled datasets. However, annotating lidar data is
resource-consuming, and depending only on supervised learning limits the
applicability of trained models. Self-supervised training strategies can
alleviate these issues by learning a general point cloud backbone model for
downstream 3D vision tasks. Against this backdrop, we show the relationship
between self-supervised multi-frame flow representations and single-frame 3D
detection hypotheses. Our main contribution leverages learned flow and motion
representations and combines a self-supervised backbone with a supervised 3D
detection head. First, a self-supervised scene flow estimation model is trained
with cycle consistency. Then, the point cloud encoder of this model is used as
the backbone of a single-frame 3D object detection head model. This second 3D
object detection model learns to utilize motion representations to distinguish
dynamic objects exhibiting different movement patterns. Experiments on KITTI
and nuScenes benchmarks show that the proposed self-supervised pre-training
increases 3D detection performance significantly.
https://github.com/emecercelik/ssl-3d-detection.git
