# RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving

arXiv: https://arxiv.org/abs/1911.09712 · HTML: https://mlsanity.com/paper/1911.09712/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 23 of 23)

- [Is Pseudo-Lidar needed for Monocular 3D Object detection?](https://mlsanity.com/paper/2108.06417/) (2021)
- [AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection](https://mlsanity.com/paper/2108.11127/) (2021)
- [Monocular 3D Object Detection: An Extrinsic Parameter Free Approach](https://mlsanity.com/paper/2106.15796/) (2021)
- [Ground-aware Monocular 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2102.00690/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [M3DSSD: Monocular 3D Single Stage Object Detector](https://mlsanity.com/paper/2103.13164/) (2021)
- [Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion](https://mlsanity.com/paper/2103.16690/) (2021)
- [YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection](https://mlsanity.com/paper/2103.09422/) (2021)
- [Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2203.02112/) (2022)
- [ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2303.15334/) (2023)
- [MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation](https://mlsanity.com/paper/2110.00464/) (2021)
- [IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image](https://mlsanity.com/paper/2103.03480/) (2021)
- [SGM3D: Stereo Guided Monocular 3D Object Detection](https://mlsanity.com/paper/2112.01914/) (2021)
- [Lite-FPN for Keypoint-based Monocular 3D Object Detection](https://mlsanity.com/paper/2105.00268/) (2021)
- [Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking](https://mlsanity.com/paper/2206.03666/) (2022)
- [MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation](https://mlsanity.com/paper/2303.13561/) (2023)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning](https://mlsanity.com/paper/2306.01980/) (2023)
- [Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training](https://mlsanity.com/paper/2310.00920/) (2023)
- [Depth Is All You Need for Monocular 3D Detection](https://mlsanity.com/paper/2210.02493/) (2022)
- [Depth Refinement for Improved Stereo Reconstruction](https://mlsanity.com/paper/2112.08070/) (2021)
- [SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection](https://mlsanity.com/paper/2307.02270/) (2023)
- [Scalable Vision-Based 3D Object Detection and Monocular Depth Estimation for Autonomous Driving](https://mlsanity.com/paper/2403.02037/) (2024)
- [S-LAM3D: Segmentation-Guided Monocular 3D Object Detection via Feature Space Fusion](https://mlsanity.com/paper/2509.05999/) (2025)

## Abstract (arXiv)

In this paper, we strive for solving the ambiguities arisen by the astoundingly high density of raw PseudoLiDAR for monocular 3D object detection for autonomous driving. Without much computational overhead, we propose a supervised and an unsupervised sparsification scheme of PseudoLiDAR prior to 3D detection. Both the strategies assist the standard 3D detector gain better performance over the raw PseudoLiDAR baseline using only ~5% of its points on the KITTI object detection benchmark, thus making our monocular framework and LiDAR-based counterparts computationally equivalent (Figure 1). Moreover, our architecture agnostic refinements provide state-of-the-art results on KITTI3D test set for "Car" and "Pedestrian" categories with 54% relative improvement for "Pedestrian". Finally, exploratory analysis is performed on the discrepancy between monocular and LiDAR-based 3D detection frameworks to guide future endeavours.