# Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations

arXiv: https://arxiv.org/abs/2008.08766 · HTML: https://mlsanity.com/paper/2008.08766/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 10 of 10)

- [Focal Sparse Convolutional Networks for 3D Object Detection](https://mlsanity.com/paper/2204.12463/) (2022)
- [SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud](https://mlsanity.com/paper/2104.09804/) (2021)
- [PointCutMix: Regularization Strategy for Point Cloud Classification](https://mlsanity.com/paper/2101.01461/) (2021)
- [Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection](https://mlsanity.com/paper/2107.07004/) (2021)
- [Spatial Pruned Sparse Convolution for Efficient 3D Object Detection](https://mlsanity.com/paper/2209.14201/) (2022)
- [SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud](https://mlsanity.com/paper/2103.15396/) (2021)
- [Multimodal 3D Object Detection on Unseen Domains](https://mlsanity.com/paper/2404.11764/) (2024)
- [Co-Fix3D: Enhancing 3D Object Detection with Collaborative Refinement](https://mlsanity.com/paper/2408.07999/) (2024)
- [Structure Information is the Key: Self-Attention RoI Feature Extractor in 3D Object Detection](https://mlsanity.com/paper/2111.00931/) (2021)
- [PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion](https://mlsanity.com/paper/2212.06244/) (2022)

## Abstract (arXiv)

We present Deformable PV-RCNN, a high-performing point-cloud based 3D object detector. Currently, the proposal refinement methods used by the state-of-the-art two-stage detectors cannot adequately accommodate differing object scales, varying point-cloud density, part-deformation and clutter. We present a proposal refinement module inspired by 2D deformable convolution networks that can adaptively gather instance-specific features from locations where informative content exists. We also propose a simple context gating mechanism which allows the keypoints to select relevant context information for the refinement stage. We show state-of-the-art results on the KITTI dataset.