ARM3D: Attention-based relation module for indoor 3D object detection

TL;DR

ARM3D introduces an attention-based relation module that enhances 3D object detection by focusing on relevant relation contexts, reducing noise and ambiguity for more accurate and robust results.

Contribution

The paper proposes a novel ARM3D module that uses attention mechanisms to selectively extract useful relation context in 3D detection, improving performance over existing methods.

Findings

ARM3D improves detection accuracy when integrated into state-of-the-art detectors.

ARM3D demonstrates robustness and generalization across various 3D detection scenarios.

The module effectively filters out noisy relation contexts, reducing scene ambiguity.

Abstract

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.