Recent Transformer-based 3D object detectors learn point cloud features
either from point- or voxel-based representations. However, the former requires
time-consuming sampling while the latter introduces quantization errors. In
this paper, we present a novel Point-Voxel Transformer for single-stage 3D
detection (PVT-SSD) that takes advantage of these two representations.
Specifically, we first use voxel-based sparse convolutions for efficient
feature encoding. Then, we propose a Point-Voxel Transformer (PVT) module that
obtains long-range contexts in a cheap manner from voxels while attaining
accurate positions from points. The key to associating the two different
representations is our introduced input-dependent Query Initialization module,
which could efficiently generate reference points and content queries. Then,
PVT adaptively fuses long-range contextual and local geometric information
around reference points into content queries. Further, to quickly find the
neighboring points of reference points, we design the Virtual Range Image
module, which generalizes the native range image to multi-sensor and
multi-frame. The experiments on several autonomous driving benchmarks verify
the effectiveness and efficiency of the proposed method. Code will be available
at this https URL

本论文提供了一种新型的点块 Transformer 用于单级三维检测 (PVT-SSD), 其使用基于体素的稀疏卷积进行有效的特征编码，并从体素中以便宜的方式获取长距离上下文，同时从点中获取准确的位置并通过引入依赖于输入的查询初始化模块关联这两种不同的表示。进一步地，通过设计 Virtual Range Image 模块，该方法能够快速找到参考点的邻近点。该方法在几个自动驾驶基准测试中得到了验证，表明其有效性和高效性。