  3D object detection has been wildly studied in recent years, especially for
robot perception systems. However, existing 3D object detection is under a
closed-set condition, meaning that the network can only output boxes of trained
classes. Unfortunately, this closed-set condition is not robust enough for
practical use, as it will identify unknown objects as known by mistake.
Therefore, in this paper, we propose an open-set 3D object detector, which aims
to (1) identify known objects, like the closed-set detection, and (2) identify
unknown objects and give their accurate bounding boxes. Specifically, we divide
the open-set 3D object detection problem into two steps: (1) finding out the
regions containing the unknown objects with high probability and (2) enclosing
the points of these regions with proper bounding boxes. The first step is
solved by the finding that unknown objects are often classified as known
objects with low confidence, and we show that the Euclidean distance sum based
on metric learning is a better confidence score than the naive softmax
probability to differentiate unknown objects from known objects. On this basis,
unsupervised clustering is used to refine the bounding boxes of unknown
objects. The proposed method combining metric learning and unsupervised
clustering is called the MLUC network. Our experiments show that our MLUC
network achieves state-of-the-art performance and can identify both known and
unknown objects as expected.
