  We introduce Displacement Aware Relation Module (DisARM), a novel neural
network module for enhancing the performance of 3D object detection in point
cloud scenes. The core idea of our method is that contextual information is
critical to tell the difference when the instance geometry is incomplete or
featureless. We find that relations between proposals provide a good
representation to describe the context. However, adopting relations between all
the object or patch proposals for detection is inefficient, and an imbalanced
combination of local and global relations brings extra noise that could mislead
the training. Rather than working with all relations, we found that training
with relations only between the most representative ones, or anchors, can
significantly boost the detection performance. A good anchor should be
semantic-aware with no ambiguity and independent with other anchors as well. To
find the anchors, we first perform a preliminary relation anchor module with an
objectness-aware sampling approach and then devise a displacement-based module
for weighing the relation importance for better utilization of contextual
information. This lightweight relation module leads to significantly higher
accuracy of object instance detection when being plugged into the
state-of-the-art detectors. Evaluations on the public benchmarks of real-world
scenes show that our method achieves state-of-the-art performance on both SUN
RGB-D and ScanNet V2.
