# TC-SfM: Robust Track-Community-Based Structure-from-Motion

arXiv: https://arxiv.org/abs/2206.05866 · HTML: https://mlsanity.com/paper/2206.05866/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Pixel-Perfect Structure-from-Motion with Featuremetric Refinement](https://mlsanity.com/paper/2108.08291/) (2021)
- [Robust Camera Location Estimation by Convex Programming](https://mlsanity.com/paper/1412.0165/) (2014)
- [Baseline Desensitizing In Translation Averaging](https://mlsanity.com/paper/1901.00643/) (2019)
- [Graph-Based Parallel Large Scale Structure from Motion](https://mlsanity.com/paper/1912.10659/) (2019)
- [Large Scale SfM with the Distributed Camera Model](https://mlsanity.com/paper/1607.03949/) (2016)
- [ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion](https://mlsanity.com/paper/1510.08012/) (2015)
- [CSfM: Community-based Structure from Motion](https://mlsanity.com/paper/1803.08716/) (2018)

## Built on by (most cited first, 6 of 6)

- [Detector-Free Structure from Motion](https://mlsanity.com/paper/2306.15669/) (2023)
- [Grounded Vision-Language Navigation for UAVs with Open-Vocabulary Goal Understanding](https://mlsanity.com/paper/2506.10756/) (2025)
- [Level-S2fM: Structure from Motion on Neural Level Set of Implicit Surfaces](https://mlsanity.com/paper/2211.12018/) (2022)
- [Oracle-Guided Masked Contrastive Reinforcement Learning for Visuomotor Policies](https://mlsanity.com/paper/2510.05692/) (2025)
- [GaussFly: Contrastive Reinforcement Learning for Visuomotor Policies in 3D Gaussian Fields](https://mlsanity.com/paper/2604.05062/) (2026)
- [Robust Global Structure-from-Motion via View Graph Pruning](https://mlsanity.com/paper/2608.22054/) (2026)

## Abstract (arXiv)

Structure-from-Motion (SfM) aims to recover 3D scene structures and camera poses based on the correspondences between input images, and thus the ambiguity caused by duplicate structures (i.e., different structures with strong visual resemblance) always results in incorrect camera poses and 3D structures. To deal with the ambiguity, most existing studies resort to additional constraint information or implicit inference by analyzing two-view geometries or feature points. In this paper, we propose to exploit high-level information in the scene, i.e., the spatial contextual information of local regions, to guide the reconstruction. Specifically, a novel structure is proposed, namely, textittrack-community, in which each community consists of a group of tracks and represents a local segment in the scene. A community detection algorithm is used to partition the scene into several segments. Then, the potential ambiguous segments are detected by analyzing the neighborhood of tracks and corrected by checking the pose consistency. Finally, we perform partial reconstruction on each segment and align them with a novel bidirectional consistency cost function which considers both 3D-3D correspondences and pairwise relative camera poses. Experimental results demonstrate that our approach can robustly alleviate reconstruction failure resulting from visually indistinguishable structures and accurately merge the partial reconstructions.