# Scalable Dense Monocular Surface Reconstruction

arXiv: https://arxiv.org/abs/1710.06130 · HTML: https://mlsanity.com/paper/1710.06130/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2017.

## Built on by (most cited first, 7 of 7)

- [State of the Art in Dense Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/2210.15664/) (2022)
- [Unbiased 4D: Monocular 4D Reconstruction with a Neural Deformation Model](https://mlsanity.com/paper/2206.08368/) (2022)
- [φ-SfT: Shape-from-Template with a Physics-Based Deformation Model](https://mlsanity.com/paper/2203.11938/) (2022)
- [Organic Priors in Non-Rigid Structure from Motion](https://mlsanity.com/paper/2207.06262/) (2022)
- [Thin-Shell-SfT: Fine-Grained Monocular Non-rigid 3D Surface Tracking with Neural Deformation Fields](https://mlsanity.com/paper/2503.19976/) (2025)
- [Non-Rigid Structure-from-Motion via Differential Geometry with Recoverable Conformal Scale](https://mlsanity.com/paper/2510.01665/) (2025)
- [HeadRecon: High-Fidelity 3D Head Reconstruction from Monocular Video](https://mlsanity.com/paper/2312.08863/) (2023)

## Abstract (arXiv)

This paper reports on a novel template-free monocular non-rigid surface reconstruction approach. Existing techniques using motion and deformation cues rely on multiple prior assumptions, are often computationally expensive and do not perform equally well across the variety of data sets. In contrast, the proposed Scalable Monocular Surface Reconstruction (SMSR) combines strengths of several algorithms, i.e., it is scalable with the number of points, can handle sparse and dense settings as well as different types of motions and deformations. We estimate camera pose by singular value thresholding and proximal gradient. Our formulation adopts alternating direction method of multipliers which converges in linear time for large point track matrices. In the proposed SMSR, trajectory space constraints are integrated by smoothing of the measurement matrix. In the extensive experiments, SMSR is demonstrated to consistently achieve state-of-the-art accuracy on a wide variety of data sets.