Title: Enhancing Single-Stage Fully Sparse 3D Object Detection Through Shape-Prior-Driven Point Cloud Completion

Abstract:

In autonomous driving applications, single-stage fully sparse 3D object detectors utilize point cloud data to identify objects. Nevertheless, the inherent sparsity and incompleteness of these point clouds often constrain detection accuracy. To mitigate this limitation, we present a specialized point cloud completion technique tailored for single-stage fully sparse detectors. This shape-prior-based completion framework operates through two sequential phases. First, we introduce an innovative Instance Selection module that successfully isolates point clouds belonging to foreground objects, even in the absence of proposals from the baseline model, while simultaneously filtering out background noise. Second, we propose an Alignment-Based Point Completion module that aligns foreground object point clouds with predefined prototypes based on both orientation and center position. Following this alignment, missing regions of the foreground objects are reconstructed by selecting points from the corresponding prototypes. We assessed the efficacy of our approach on two single-stage fully sparse detectors using the KITTI dataset. Our experimental findings indicate that the proposed method yields substantial gains in detection performance, thereby validating its effectiveness and generalizability.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC