  Relation context has been proved to be useful for many challenging vision
tasks. In the field of 3D object detection, previous methods have been taking
the advantage of context encoding, graph embedding, or explicit relation
reasoning to extract relation context. However, there exists inevitably
redundant relation context due to noisy or low-quality proposals. In fact,
invalid relation context usually indicates underlying scene misunderstanding
and ambiguity, which may, on the contrary, reduce the performance in complex
scenes. Inspired by recent attention mechanism like Transformer, we propose a
novel 3D attention-based relation module (ARM3D). It encompasses object-aware
relation reasoning to extract pair-wise relation contexts among qualified
proposals and an attention module to distribute attention weights towards
different relation contexts. In this way, ARM3D can take full advantage of the
useful relation context and filter those less relevant or even confusing
contexts, which mitigates the ambiguity in detection. We have evaluated the
effectiveness of ARM3D by plugging it into several state-of-the-art 3D object
detectors and showing more accurate and robust detection results. Extensive
experiments show the capability and generalization of ARM3D on 3D object
detection. Our source code is available at https://github.com/lanlan96/ARM3D.

Computer Vision and Pattern Recognition