# Lidar Point Cloud Guided Monocular 3D Object Detection

arXiv: https://arxiv.org/abs/2104.09035 · HTML: https://mlsanity.com/paper/2104.09035/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 56 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [3DSSD: Point-based 3D Single Stage Object Detector](https://mlsanity.com/paper/2002.10187/) (2020)
- [From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://mlsanity.com/paper/1907.10326/) (2019)
- [From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network](https://mlsanity.com/paper/1907.03670/) (2019)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [Categorical Depth Distribution Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.01100/) (2021)
- [3D Bounding Box Estimation Using Deep Learning and Geometry](https://mlsanity.com/paper/1612.00496/) (2016)
- [M3D-RPN: Monocular 3D Region Proposal Network for Object Detection](https://mlsanity.com/paper/1907.06038/) (2019)
- [Disentangling Monocular 3D Object Detection](https://mlsanity.com/paper/1905.12365/) (2019)
- [Orthographic Feature Transform for Monocular 3D Object Detection](https://mlsanity.com/paper/1811.08188/) (2018)
- [Objects are Different: Flexible Monocular 3D Object Detection](https://mlsanity.com/paper/2104.02323/) (2021)
- [Learning Depth-Guided Convolutions for Monocular 3D Object Detection](https://mlsanity.com/paper/1912.04799/) (2019)
- [MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships](https://mlsanity.com/paper/2003.00504/) (2020)
- [RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving](https://mlsanity.com/paper/2001.03343/) (2020)
- [Delving into Localization Errors for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.16237/) (2021)
- [Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving](https://mlsanity.com/paper/1903.11444/) (2019)
- [ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape](https://mlsanity.com/paper/1812.02781/) (2018)
- [Kinematic 3D Object Detection in Monocular Video](https://mlsanity.com/paper/2007.09548/) (2020)
- [Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud](https://mlsanity.com/paper/1903.09847/) (2019)

## Built on by (most cited first, 29 of 29)

- [3D Object Detection from Images for Autonomous Driving: A Survey](https://mlsanity.com/paper/2202.02980/) (2022)
- [BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2211.09386/) (2022)
- [Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2211.07171/) (2022)
- [DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.08531/) (2022)
- [Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview](https://mlsanity.com/paper/2105.14291/) (2021)
- [Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2203.02112/) (2022)
- [Homography Loss for Monocular 3D Object Detection](https://mlsanity.com/paper/2204.00754/) (2022)
- [Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.04448/) (2022)
- [DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion](https://mlsanity.com/paper/2111.10332/) (2021)
- [NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization](https://mlsanity.com/paper/2305.17763/) (2023)
- [3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for Indoor 3D Object Detection](https://mlsanity.com/paper/2410.01647/) (2024)
- [Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection](https://mlsanity.com/paper/2403.17387/) (2024)
- [Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning](https://mlsanity.com/paper/2307.08415/) (2023)
- [MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection](https://mlsanity.com/paper/2404.04910/) (2024)
- [Geometric-aware Pretraining for Vision-centric 3D Object Detection](https://mlsanity.com/paper/2304.03105/) (2023)
- [Shape-Aware Monocular 3D Object Detection](https://mlsanity.com/paper/2204.08717/) (2022)
- [Segment, Lift and Fit: Automatic 3D Shape Labeling from 2D Prompts](https://mlsanity.com/paper/2407.11382/) (2024)
- [Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training](https://mlsanity.com/paper/2310.00920/) (2023)
- [Time-to-Label: Temporal Consistency for Self-Supervised Monocular 3D Object Detection](https://mlsanity.com/paper/2203.02193/) (2022)
- [Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.07933/) (2022)
- [An Empirical Study of Pseudo-Labeling for Image-based 3D Object Detection](https://mlsanity.com/paper/2208.07137/) (2022)
- [Weakly Supervised 3D Object Detection with Multi-Stage Generalization](https://mlsanity.com/paper/2306.05418/) (2023)
- [AA3DNet: Attention Augmented Real Time 3D Object Detection](https://mlsanity.com/paper/2107.12137/) (2021)
- [Towards Fair and Comprehensive Comparisons for Image-Based 3D Object Detection](https://mlsanity.com/paper/2310.05447/) (2023)
- [MonoDETRNext: Next-Generation Accurate and Efficient Monocular 3D Object Detector](https://mlsanity.com/paper/2405.15176/) (2024)
- [Recursive Cross-View: Use Only 2D Detectors to Achieve 3D Object Detection without 3D Annotations](https://mlsanity.com/paper/2211.07108/) (2022)
- [View-to-Label: Multi-View Consistency for Self-Supervised 3D Object Detection](https://mlsanity.com/paper/2305.17972/) (2023)
- [Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection](https://mlsanity.com/paper/2306.10921/) (2023)
- [Improved Single Camera BEV Perception Using Multi-Camera Training](https://mlsanity.com/paper/2409.02676/) (2024)

## Abstract (arXiv)

Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in monocular 3D detection, which is an interesting and counterintuitive finding. Using rough labels that are randomly disturbed, the detector can achieve very close accuracy compared to the one using the ground-truth labels. We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels. Motivated by the conclusions above and considering the precise LiDAR 3D measurement, we propose a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D object detection (LPCG). This framework is capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs. Specifically, It generates pseudo labels from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in 3D space, such pseudo labels can replace manually annotated labels in the training of monocular 3D detectors, since their 3D location information is precise. LPCG can be applied into any monocular 3D detector to fully use massive unlabeled data in a self-driving system. As a result, in KITTI benchmark, we take the first place on both monocular 3D and BEV (bird's-eye-view) detection with a significant margin. In Waymo benchmark, our method using 10% labeled data achieves comparable accuracy to the baseline detector using 100% labeled data. The codes are released at https://github.com/SPengLiang/LPCG.