# 3D Registration for Self-Occluded Objects in Context

arXiv: https://arxiv.org/abs/2011.11260 · HTML: https://mlsanity.com/paper/2011.11260/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [End-to-End Learning of Multi-category 3D Pose and Shape Estimation](https://mlsanity.com/paper/2112.10196/) (2021)
- [Piecewise Planar Hulls for Semi-Supervised Learning of 3D Shape and Pose from 2D Images](https://mlsanity.com/paper/2211.07491/) (2022)

## Abstract (arXiv)

While much progress has been made on the task of 3D point cloud registration, there still exists no learning-based method able to estimate the 6D pose of an object observed by a 2.5D sensor in a scene. The challenges of this scenario include the fact that most measurements are outliers depicting the object's surrounding context, and the mismatch between the complete 3D object model and its self-occluded observations. We introduce the first deep learning framework capable of effectively handling this scenario. Our method consists of an instance segmentation module followed by a pose estimation one. It allows us to perform 3D registration in a one-shot manner, without requiring an expensive iterative procedure. We further develop an on-the-fly rendering-based training strategy that is both time- and memory-efficient. Our experiments evidence the superiority of our approach over the state-of-the-art traditional and learning-based 3D registration methods.