# Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection

Source: [Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection](<https://www.sota2.com/research/paper/68fa6ed21e209131cea42677>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:07:49.028Z

Authors: Zhenyu Li, Zhipeng Zhang, Heng Fan, Yuan He, Ke Wang, Xianming Liu, Junjun Jiang

Published: 2023-03-20T16:28:15.000Z

## Abstract

In this paper, we improve the challenging monocular 3D object detection problem with a general semi-supervised framework. Specifically, having observed that the bottleneck of this task lies in lacking reliable and informative samples to train the detector, we introduce a novel, simple, yet effective \`Augment and Criticize' framework that explores abundant informative samples from unlabeled data for learning more robust detection models. In the \`Augment' stage, we present the Augmentation-based Prediction aGgregation (APG), which aggregates detections from various automatically learned augmented views to improve the robustness of pseudo label generation. Since not all pseudo labels from APG are beneficially informative, the subsequent \`Criticize' phase is presented. In particular, we introduce the Critical Retraining Strategy (CRS) that, unlike simply filtering pseudo labels using a fixed threshold (e.g., classification score) as in 2D semi-supervised tasks, leverages a learnable network to evaluate the contribution of unlabeled images at different training timestamps. This way, the noisy samples prohibitive to model evolution could be effectively suppressed. To validate our framework, we apply it to MonoDLE and MonoFlex. The two new detectors, dubbed 3DSeMo\_DLE and 3DSeMo\_FLEX, achieve state-of-the-art results with remarkable improvements for over 3.5% AP\_3D/BEV (Easy) on KITTI, showing its effectiveness and generality. Code and models will be released.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.11243>)

[PDF](<https://arxiv.org/pdf/2303.11243.pdf>)