# 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds

arXiv: https://arxiv.org/abs/2004.04962 · HTML: https://mlsanity.com/paper/2004.04962/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 15 of 15)

- [Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds](https://mlsanity.com/paper/2203.11139/) (2022)
- [Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2211.07171/) (2022)
- [DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection](https://mlsanity.com/paper/2207.10909/) (2022)
- [From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder](https://mlsanity.com/paper/2108.03648/) (2021)
- [Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud](https://mlsanity.com/paper/2108.03634/) (2021)
- [SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud](https://mlsanity.com/paper/2103.15396/) (2021)
- [SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection](https://mlsanity.com/paper/2407.01016/) (2024)
- [A Survey of Robust 3D Object Detection Methods in Point Clouds](https://mlsanity.com/paper/2204.00106/) (2022)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [iDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds](https://mlsanity.com/paper/2312.15449/) (2023)
- [CMF-IoU: Multi-Stage Cross-Modal Fusion 3D Object Detection with IoU Joint Prediction](https://mlsanity.com/paper/2508.12917/) (2025)
- [3D Object Detection Combining Semantic and Geometric Features from Point Clouds](https://mlsanity.com/paper/2110.04704/) (2021)
- [SGCCNet: Single-Stage 3D Object Detector With Saliency-Guided Data Augmentation and Confidence Correction Mechanism](https://mlsanity.com/paper/2407.01239/) (2024)
- [PDM-SSD: Single-Stage Three-Dimensional Object Detector With Point Dilation](https://mlsanity.com/paper/2502.07822/) (2025)
- [MonoLite3D: Lightweight 3D Object Properties Estimation](https://mlsanity.com/paper/2503.02201/) (2025)

## Abstract (arXiv)

Most existing point cloud based 3D object detectors focus on the tasks of classification and box regression. However, another bottleneck in this area is achieving an accurate detection confidence for the Non-Maximum Suppression (NMS) post-processing. In this paper, we add a 3D IoU prediction branch to the regular classification and regression branches. The predicted IoU is used as the detection confidence for NMS. In order to obtain a more accurate IoU prediction, we propose a 3D IoU-Net with IoU sensitive feature learning and an IoU alignment operation. To obtain a perspective-invariant prediction head, we propose an Attentive Corner Aggregation (ACA) module by aggregating a local point cloud feature from each perspective of eight corners and adaptively weighting the contribution of each perspective with different attentions. We propose a Corner Geometry Encoding (CGE) module for geometry information embedding. To the best of our knowledge, this is the first time geometric embedding information has been introduced in proposal feature learning. These two feature parts are then adaptively fused by a multi-layer perceptron (MLP) network as our IoU sensitive feature. The IoU alignment operation is introduced to resolve the mismatching between the bounding box regression head and IoU prediction, thereby further enhancing the accuracy of IoU prediction. The experimental results on the KITTI car detection benchmark show that 3D IoU-Net with IoU perception achieves state-of-the-art performance.