  Accurate 3D object detection in large-scale outdoor scenes, characterized by
considerable variations in object scales, necessitates features rich in both
long-range and fine-grained information. While recent detectors have utilized
window-based transformers to model long-range dependencies, they tend to
overlook fine-grained details. To bridge this gap, we propose MsSVT++, an
innovative Mixed-scale Sparse Voxel Transformer that simultaneously captures
both types of information through a divide-and-conquer approach. This approach
involves explicitly dividing attention heads into multiple groups, each
responsible for attending to information within a specific range. The outputs
of these groups are subsequently merged to obtain final mixed-scale features.
To mitigate the computational complexity associated with applying a
window-based transformer in 3D voxel space, we introduce a novel Chessboard
Sampling strategy and implement voxel sampling and gathering operations
sparsely using a hash map. Moreover, an important challenge stems from the
observation that non-empty voxels are primarily located on the surface of
objects, which impedes the accurate estimation of bounding boxes. To overcome
this challenge, we introduce a Center Voting module that integrates newly voted
voxels enriched with mixed-scale contextual information towards the centers of
the objects, thereby improving precise object localization. Extensive
experiments demonstrate that our single-stage detector, built upon the
foundation of MsSVT++, consistently delivers exceptional performance across
diverse datasets.
