# Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation

arXiv: https://arxiv.org/abs/2004.03572 · HTML: https://mlsanity.com/paper/2004.03572/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 30 of 43)

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer](https://mlsanity.com/paper/2203.10981/) (2022)
- [LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector](https://mlsanity.com/paper/2108.08258/) (2021)
- [Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection](https://mlsanity.com/paper/2112.04628/) (2021)
- [3D Object Detection from Images for Autonomous Driving: A Survey](https://mlsanity.com/paper/2202.02980/) (2022)
- [Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2211.07171/) (2022)
- [Monocular 3D Object Detection with Depth from Motion](https://mlsanity.com/paper/2207.12988/) (2022)
- [Context-Enhanced Stereo Transformer](https://mlsanity.com/paper/2210.11719/) (2022)
- [YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection](https://mlsanity.com/paper/2103.09422/) (2021)
- [CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization](https://mlsanity.com/paper/2503.03430/) (2025)
- [DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction](https://mlsanity.com/paper/2209.06351/) (2022)
- [PLUMENet: Efficient 3D Object Detection from Stereo Images](https://mlsanity.com/paper/2101.06594/) (2021)
- [A Simple Baseline for Multi-Camera 3D Object Detection](https://mlsanity.com/paper/2208.10035/) (2022)
- [DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking](https://mlsanity.com/paper/2303.16628/) (2023)
- [Stereo CenterNet based 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2103.11071/) (2021)
- [DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors](https://mlsanity.com/paper/2204.03039/) (2022)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning](https://mlsanity.com/paper/2306.01980/) (2023)
- [Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras](https://mlsanity.com/paper/2502.19630/) (2025)
- [ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection](https://mlsanity.com/paper/2111.14055/) (2021)
- [Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection](https://mlsanity.com/paper/2211.16779/) (2022)
- [StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection](https://mlsanity.com/paper/2301.01615/) (2023)
- [Shape-Aware Monocular 3D Object Detection](https://mlsanity.com/paper/2204.08717/) (2022)
- [Towards Model Generalization for Monocular 3D Object Detection](https://mlsanity.com/paper/2205.11664/) (2022)
- [DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment](https://mlsanity.com/paper/2209.14965/) (2022)
- [Perceiving Unseen 3D Objects by Poking the Objects](https://mlsanity.com/paper/2302.13375/) (2023)
- [Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection](https://mlsanity.com/paper/2508.02288/) (2025)
- [Stereo Neural Vernier Caliper](https://mlsanity.com/paper/2203.11018/) (2022)
- [MS23D: A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer](https://mlsanity.com/paper/2308.16518/) (2023)
- [Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning](https://mlsanity.com/paper/2312.08004/) (2023)

## Abstract (arXiv)

In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering a point cloud with disparity estimation and then apply a 3D detector. The disparity map is computed for the entire image, which is costly and fails to leverage category-specific prior. In contrast, we design an instance disparity estimation network (iDispNet) that predicts disparity only for pixels on objects of interest and learns a category-specific shape prior for more accurate disparity estimation. To address the challenge from scarcity of disparity annotation in training, we propose to use a statistical shape model to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds, which makes our system more widely applicable. Experiments on the KITTI dataset show that, even when LiDAR ground-truth is not available at training time, Disp R-CNN achieves competitive performance and outperforms previous state-of-the-art methods by 20% in terms of average precision.