  Conventional 3D object detection approaches concentrate on bounding boxes
representation learning with several parameters, i.e., localization, dimension,
and orientation. Despite its popularity and universality, such a
straightforward paradigm is sensitive to slight numerical deviations,
especially in localization. By exploiting the property that point clouds are
naturally captured on the surface of objects along with accurate location and
intensity information, we introduce a new perspective that views bounding box
regression as an implicit function. This leads to our proposed framework,
termed Implicit Detection or ImpDet, which leverages implicit field learning
for 3D object detection. Our ImpDet assigns specific values to points in
different local 3D spaces, thereby high-quality boundaries can be generated by
classifying points inside or outside the boundary. To solve the problem of
sparsity on the object surface, we further present a simple yet efficient
virtual sampling strategy to not only fill the empty region, but also learn
rich semantic features to help refine the boundaries. Extensive experimental
results on KITTI and Waymo benchmarks demonstrate the effectiveness and
robustness of unifying implicit fields into object detection.
