  Point clouds can be represented in many forms (views), typically, point-based
sets, voxel-based cells or range-based images(i.e., panoramic view). The
point-based view is geometrically accurate, but it is disordered, which makes
it difficult to find local neighbors efficiently. The voxel-based view is
regular, but sparse, and computation grows cubically when voxel resolution
increases. The range-based view is regular and generally dense, however
spherical projection makes physical dimensions distorted. Both voxel- and
range-based views suffer from quantization loss, especially for voxels when
facing large-scale scenes. In order to utilize different view's advantages and
alleviate their own shortcomings in fine-grained segmentation task, we propose
a novel range-point-voxel fusion network, namely RPVNet. In this network, we
devise a deep fusion framework with multiple and mutual information
interactions among these three views and propose a gated fusion module (termed
as GFM), which can adaptively merge the three features based on concurrent
inputs. Moreover, the proposed RPV interaction mechanism is highly efficient,
and we summarize it into a more general formulation. By leveraging this
efficient interaction and relatively lower voxel resolution, our method is also
proved to be more efficient. Finally, we evaluated the proposed model on two
large-scale datasets, i.e., SemanticKITTI and nuScenes, and it shows
state-of-the-art performance on both of them. Note that, our method currently
ranks 1st on SemanticKITTI leaderboard without any extra tricks.
