  In this paper, we address the 3D object detection task by capturing
multi-level contextual information with the self-attention mechanism and
multi-scale feature fusion. Most existing 3D object detection methods recognize
objects individually, without giving any consideration on contextual
information between these objects. Comparatively, we propose Multi-Level
Context VoteNet (MLCVNet) to recognize 3D objects correlatively, building on
the state-of-the-art VoteNet. We introduce three context modules into the
voting and classifying stages of VoteNet to encode contextual information at
different levels. Specifically, a Patch-to-Patch Context (PPC) module is
employed to capture contextual information between the point patches, before
voting for their corresponding object centroid points. Subsequently, an
Object-to-Object Context (OOC) module is incorporated before the proposal and
classification stage, to capture the contextual information between object
candidates. Finally, a Global Scene Context (GSC) module is designed to learn
the global scene context. We demonstrate these by capturing contextual
information at patch, object and scene levels. Our method is an effective way
to promote detection accuracy, achieving new state-of-the-art detection
performance on challenging 3D object detection datasets, i.e., SUN RGBD and
ScanNet. We also release our code at https://github.com/NUAAXQ/MLCVNet.
