  A bathtub in a library, a sink in an office, a bed in a laundry room -- the
counter-intuition suggests that scene provides important prior knowledge for 3D
object detection, which instructs to eliminate the ambiguous detection of
similar objects. In this paper, we propose HyperDet3D to explore
scene-conditioned prior knowledge for 3D object detection. Existing methods
strive for better representation of local elements and their relations without
scene-conditioned knowledge, which may cause ambiguity merely based on the
understanding of individual points and object candidates. Instead, HyperDet3D
simultaneously learns scene-agnostic embeddings and scene-specific knowledge
through scene-conditioned hypernetworks. More specifically, our HyperDet3D not
only explores the sharable abstracts from various 3D scenes, but also adapts
the detector to the given scene at test time. We propose a discriminative
Multi-head Scene-specific Attention (MSA) module to dynamically control the
layer parameters of the detector conditioned on the fusion of scene-conditioned
knowledge. Our HyperDet3D achieves state-of-the-art results on the 3D object
detection benchmark of the ScanNet and SUN RGB-D datasets. Moreover, through
cross-dataset evaluation, we show the acquired scene-conditioned prior
knowledge still takes effect when facing 3D scenes with domain gap.
