  The automotive mmWave radar plays a key role in advanced driver assistance
systems (ADAS) and autonomous driving. Deep learning-based instance
segmentation enables real-time object identification from the radar detection
points. In the conventional training process, accurate annotation is the key.
However, high-quality annotations of radar detection points are challenging to
achieve due to their ambiguity and sparsity. To address this issue, we propose
a contrastive learning approach for implementing radar detection points-based
instance segmentation. We define the positive and negative samples according to
the ground-truth label, apply the contrastive loss to train the model first,
and then perform fine-tuning for the following downstream task. In addition,
these two steps can be merged into one, and pseudo labels can be generated for
the unlabeled data to improve the performance further. Thus, there are four
different training settings for our method. Experiments show that when the
ground-truth information is only available for a small proportion of the
training data, our method still achieves a comparable performance to the
approach trained in a supervised manner with 100% ground-truth information.
