# Going Further with Point Pair Features

arXiv: https://arxiv.org/abs/1711.04061 · HTML: https://mlsanity.com/paper/1711.04061/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2017.

## Built on by (most cited first, 28 of 28)

- [DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency](https://mlsanity.com/paper/2103.06526/) (2021)
- [FS6D: Few-Shot 6D Pose Estimation of Novel Objects](https://mlsanity.com/paper/2203.14628/) (2022)
- [HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation](https://mlsanity.com/paper/2303.15743/) (2023)
- [Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation](https://mlsanity.com/paper/2203.10339/) (2022)
- [DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation](https://mlsanity.com/paper/2207.02805/) (2022)
- [CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild](https://mlsanity.com/paper/2203.03089/) (2022)
- [ZePHyR: Zero-shot Pose Hypothesis Rating](https://mlsanity.com/paper/2104.13526/) (2021)
- [RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration](https://mlsanity.com/paper/2209.13252/) (2022)
- [Neural Correspondence Field for Object Pose Estimation](https://mlsanity.com/paper/2208.00113/) (2022)
- [DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration](https://mlsanity.com/paper/2112.02849/) (2021)
- [Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Images](https://mlsanity.com/paper/2306.07598/) (2023)
- [Deep Fusion Transformer Network with Weighted Vector-Wise Keypoints Voting for Robust 6D Object Pose Estimation](https://mlsanity.com/paper/2308.05438/) (2023)
- [Multiway Point Cloud Mosaicking with Diffusion and Global Optimization](https://mlsanity.com/paper/2404.00429/) (2024)
- [PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching](https://mlsanity.com/paper/2304.01382/) (2023)
- [Estimating 3D Motion and Forces of Human-Object Interactions from Internet Videos](https://mlsanity.com/paper/2111.01591/) (2021)
- [OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation](https://mlsanity.com/paper/2201.07309/) (2022)
- [Depth-based 6DoF Object Pose Estimation using Swin Transformer](https://mlsanity.com/paper/2303.02133/) (2023)
- [GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement](https://mlsanity.com/paper/2404.11139/) (2024)
- [SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation](https://mlsanity.com/paper/2208.08338/) (2022)
- [Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation](https://mlsanity.com/paper/2408.08234/) (2024)
- [Instance by Instance: An Iterative Framework for Multi-instance 3D Registration](https://mlsanity.com/paper/2402.04195/) (2024)
- [Incremental Multiview Point Cloud Registration with Two-stage Candidate Retrieval](https://mlsanity.com/paper/2407.07525/) (2024)
- [6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping](https://mlsanity.com/paper/2111.06276/) (2021)
- [6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features](https://mlsanity.com/paper/2209.08266/) (2022)
- [GAFAR: Graph-Attention Feature-Augmentation for Registration A Fast and Light-weight Point Set Registration Algorithm](https://mlsanity.com/paper/2307.02339/) (2023)
- [Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices](https://mlsanity.com/paper/2406.02977/) (2024)
- [Enhancing Transparent Object Pose Estimation: A Fusion of GDR-Net and Edge Detection](https://mlsanity.com/paper/2502.12027/) (2025)
- [Rotational Symmetry based Object Pose Estimation from Point Clouds in the Absence of Known 3D Models](https://mlsanity.com/paper/2606.16593/) (2026)

## Abstract (arXiv)

Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.