The single-stage point-based 3D object detectors have attracted widespread research interest due to their advantages of lightweight and fast inference speed. However, they still face challenges such as inadequate learning of low-quality objects (ILQ) and misalignment between localization accuracy and classification confidence (MLC). In this paper, we propose SGCCNet to alleviate these two issues. For ILQ, SGCCNet adopts a Saliency-Guided Data Augmentation (SGDA) strategy to enhance the robustness of the model on low-quality objects by reducing its reliance on salient features. Specifically, We construct a classification task and then approximate the saliency scores of points by moving points towards the point cloud centroid in a differentiable process. During the training process, SGCCNet will be forced to learn from low saliency features through dropping points. Meanwhile, to avoid internal covariate shift and contextual features forgetting caused by dropping points, we add a geometric normalization module and skip connection block in each stage. For MLC, we design a Confidence Correction Mechanism (CCM) specifically for point-based multi-class detectors. This mechanism corrects the confidence of the current proposal by utilizing the predictions of other key points within the local region in the post-processing stage. Extensive experiments on the KITTI dataset demonstrate the generality and effectiveness of our SGCCNet. On the KITTI \textit{test} set, SGCCNet achieves $80.82\%$ for the metric of $AP_{3D}$ on the \textit{Moderate} level, outperforming all other point-based detectors, surpassing IA-SSD and Fast Point R-CNN by $2.35\%$ and $3.42\%$, respectively. Additionally, SGCCNet demonstrates excellent portability for other point-based detectors

提出了 SGCCNet 用于解决单阶段基于点的 3D 对象检测器的低质量对象学习和定位准确性与分类置信度不一致的问题。通过采用显著性引导数据增强和置信度校正机制来提升模型的性能，实验证明 SGCCNet 在 KITTI 数据集上的效果优于其他点云检测器，达到了 80.82% 的 3D 平均精度 (AP_3D)。

SGCCNet: 基于显著引导数据增强和置信修正机制的单阶段三维物体检测器