# Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving

arXiv: https://arxiv.org/abs/2203.02112 · HTML: https://mlsanity.com/paper/2203.02112/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 55 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 30 of 47)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)
- [Categorical Depth Distribution Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.01100/) (2021)
- [M3D-RPN: Monocular 3D Region Proposal Network for Object Detection](https://mlsanity.com/paper/1907.06038/) (2019)
- [Is Pseudo-Lidar needed for Monocular 3D Object detection?](https://mlsanity.com/paper/2108.06417/) (2021)
- [Disentangling Monocular 3D Object Detection](https://mlsanity.com/paper/1905.12365/) (2019)
- [Atlas: End-to-End 3D Scene Reconstruction from Posed Images](https://mlsanity.com/paper/2003.10432/) (2020)
- [AANet: Adaptive Aggregation Network for Efficient Stereo Matching](https://mlsanity.com/paper/2004.09548/) (2020)
- [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/1906.06310/) (2019)
- [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)
- [ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection](https://mlsanity.com/paper/2106.01178/) (2021)
- [Geometry Uncertainty Projection Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2107.13774/) (2021)
- [MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships](https://mlsanity.com/paper/2003.00504/) (2020)
- [Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries](https://mlsanity.com/paper/1803.08673/) (2018)
- [Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving](https://mlsanity.com/paper/1903.11444/) (2019)
- [HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching](https://mlsanity.com/paper/2007.12140/) (2020)
- [Rethinking Pseudo-LiDAR Representation](https://mlsanity.com/paper/2008.04582/) (2020)
- [GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving](https://mlsanity.com/paper/1903.10955/) (2019)
- [Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction](https://mlsanity.com/paper/1904.01690/) (2019)
- [DSGN: Deep Stereo Geometry Network for 3D Object Detection](https://mlsanity.com/paper/2001.03398/) (2020)
- [Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image](https://mlsanity.com/paper/1703.07570/) (2017)
- [AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection](https://mlsanity.com/paper/2108.11127/) (2021)
- [Geometry-based Distance Decomposition for Monocular 3D Object Detection](https://mlsanity.com/paper/2104.03775/) (2021)
- [MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation](https://mlsanity.com/paper/2103.12605/) (2021)

## Built on by (most cited first, 30 of 30)

- [Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild](https://mlsanity.com/paper/2207.10660/) (2022)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2211.07171/) (2022)
- [ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer](https://mlsanity.com/paper/2302.14435/) (2023)
- [Learning Occupancy for Monocular 3D Object Detection](https://mlsanity.com/paper/2305.15694/) (2023)
- [MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection](https://mlsanity.com/paper/2308.09421/) (2023)
- [NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization](https://mlsanity.com/paper/2305.17763/) (2023)
- [Delving into Motion-Aware Matching for Monocular 3D Object Tracking](https://mlsanity.com/paper/2308.11607/) (2023)
- [Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver](https://mlsanity.com/paper/2304.01289/) (2023)
- [Depth-discriminative Metric Learning for Monocular 3D Object Detection](https://mlsanity.com/paper/2401.01075/) (2024)
- [Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras](https://mlsanity.com/paper/2502.19630/) (2025)
- [Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection](https://mlsanity.com/paper/2211.16779/) (2022)
- [Multi-View Attentive Contextualization for Multi-View 3D Object Detection](https://mlsanity.com/paper/2405.12200/) (2024)
- [Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator](https://mlsanity.com/paper/2403.03468/) (2024)
- [VRM: Knowledge Distillation via Virtual Relation Matching](https://mlsanity.com/paper/2502.20760/) (2025)
- [CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting](https://mlsanity.com/paper/2309.16140/) (2023)
- [GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2310.15624/) (2023)
- [Depth Is All You Need for Monocular 3D Detection](https://mlsanity.com/paper/2210.02493/) (2022)
- [Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection](https://mlsanity.com/paper/2508.02288/) (2025)
- [Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection](https://mlsanity.com/paper/2207.07933/) (2022)
- [Sparse Points to Dense Clouds: Enhancing 3D Detection with Limited LiDAR Data](https://mlsanity.com/paper/2404.06715/) (2024)
- [Towards Fair and Comprehensive Comparisons for Image-Based 3D Object Detection](https://mlsanity.com/paper/2310.05447/) (2023)
- [CheapNVS: Real-Time On-Device Narrow-Baseline Novel View Synthesis](https://mlsanity.com/paper/2501.14533/) (2025)
- [SNNSIR: A Simple Spiking Neural Network for Stereo Image Restoration](https://mlsanity.com/paper/2508.12271/) (2025)
- [LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection](https://mlsanity.com/paper/2512.05663/) (2025)
- [Recursive Cross-View: Use Only 2D Detectors to Achieve 3D Object Detection without 3D Annotations](https://mlsanity.com/paper/2211.07108/) (2022)
- [Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection](https://mlsanity.com/paper/2306.10921/) (2023)
- [SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection](https://mlsanity.com/paper/2307.02270/) (2023)
- [Lightweight Temporal Transformer Decomposition for Federated Autonomous Driving](https://mlsanity.com/paper/2506.23523/) (2025)
- [Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs](https://mlsanity.com/paper/2507.18517/) (2025)

## Abstract (arXiv)

Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-based 3D detection architectures. The advanced stereo 3D detectors can also accurately localize 3D objects. The gap in image-to-image generation for stereo views is much smaller than that in image-to-LiDAR generation. Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for detecting 3D objects from a single image. Our analysis of depth-aware learning shows that the depth loss is effective in only feature-level virtual view generation and the estimated depth map is effective in both image-level and feature-level in our framework. We propose a disparity-wise dynamic convolution with dynamic kernels sampled from the disparity feature map to filter the features adaptively from a single image for generating virtual image features, which eases the feature degradation caused by the depth estimation errors. Till submission (November 18, 2021), our Pseudo-Stereo 3D detection framework ranks 1st on car, pedestrian, and cyclist among the monocular 3D detectors with publications on the KITTI-3D benchmark. The code is released at https://github.com/revisitq/Pseudo-Stereo-3D.