MT-SSD: Single-Stage 3D Object Detector Based on Magnification Transformation

Abstract

3D object detection plays a pivotal role in autonomous driving. Although single-stage detectors excel in speed, they often fall short in accuracy. We have identified two main issues. First, there is a significant discrepancy in prediction accuracy across different Intersection over Union (IoU) thresholds, indicating the presence of localization errors within the model. Second, traditional point-based detection models rely heavily on 1×1 convolution operations at the Set Abstraction layer, neglecting the relationship between adjacent points. To address these issues, we present the Magnification Transformation Single-Stage Detector (MT-SSD), featuring an innovative magnification Linear Transformation Module. This module applies a linear transformation to the original point cloud, sampling radius, and object labels, magnifying the error between model predictions and true values. During inference, an inverse linear transformation is applied to the detections to achieve accurate object localization. Moreover, MT-SSD introduces the Contextual Set Abstraction (CSA) layer, incorporating 1×N convolutions within the Set Abstraction layer to achieve more thorough aggregation of features among neighboring points. Our comprehensive evaluations on various autonomous driving datasets validate MT-SSD's superior performance and efficiency. Particularly noteworthy is its achievement on the Waymo Open Dataset, where MT-SSD establishes new benchmarks in single-stage 3D object detection, setting a series of state-of-the-art records. The code is available at https://github.com/qifeng22/MT-SSD .