# Reconstructing Small 3D Objects in front of a Textured Background

arXiv: https://arxiv.org/abs/2105.11352 · HTML: https://mlsanity.com/paper/2105.11352/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 75 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 9 of 9)

- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [Neighbourhood Consensus Networks](https://mlsanity.com/paper/1810.10510/) (2018)
- [Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames](https://mlsanity.com/paper/1708.04398/) (2017)
- [Multi-Class Model Fitting by Energy Minimization and Mode-Seeking](https://mlsanity.com/paper/1706.00827/) (2017)
- [Multi-body Non-rigid Structure-from-Motion](https://mlsanity.com/paper/1607.04515/) (2016)
- [Motion Segmentation by Exploiting Complementary Geometric Models](https://mlsanity.com/paper/1804.02142/) (2018)
- [Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data](https://mlsanity.com/paper/1509.02649/) (2015)
- [A Photogrammetry-based Framework to Facilitate Image-based Modeling and Automatic Camera Tracking](https://mlsanity.com/paper/2012.01044/) (2020)
- [Robust Motion Segmentation from Pairwise Matches](https://mlsanity.com/paper/1905.09043/) (2019)

## Abstract (arXiv)

We present a technique for a complete 3D reconstruction of small objects moving in front of a textured background. It is a particular variation of multibody structure from motion, which specializes to two objects only. The scene is captured in several static configurations between which the relative pose of the two objects may change. We reconstruct every static configuration individually and segment the points locally by finding multiple poses of cameras that capture the scene's other configurations. Then, the local segmentation results are combined, and the reconstructions are merged into the resulting model of the scene. In experiments with real artifacts, we show that our approach has practical advantages when reconstructing 3D objects from all sides. In this setting, our method outperforms the state-of-the-art. We integrate our method into the state of the art 3D reconstruction pipeline COLMAP.