# Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation from Single and Multiple Images

arXiv: https://arxiv.org/abs/1607.07129 · HTML: https://mlsanity.com/paper/1607.07129/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2016.

## Built on by (most cited first, 3 of 3)

- [Recurrence-based Vanishing Point Detection](https://mlsanity.com/paper/2412.20666/) (2024)
- [Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds](https://mlsanity.com/paper/2106.16129/) (2021)
- [Towards Human-Level 3D Relative Pose Estimation: Generalizable, Training-Free, with Single Reference](https://mlsanity.com/paper/2406.18453/) (2024)

## Abstract (arXiv)

Many man-made objects have intrinsic symmetries and Manhattan structure. By assuming an orthographic projection model, this paper addresses the estimation of 3D structures and camera projection using symmetry and/or Manhattan structure cues, which occur when the input is single- or multiple-image from the same category, e.g., multiple different cars. Specifically, analysis on the single image case implies that Manhattan alone is sufficient to recover the camera projection, and then the 3D structure can be reconstructed uniquely exploiting symmetry. However, Manhattan structure can be difficult to observe from a single image due to occlusion. To this end, we extend to the multiple-image case which can also exploit symmetry but does not require Manhattan axes. We propose a novel rigid structure from motion method, exploiting symmetry and using multiple images from the same category as input. Experimental results on the Pascal3D+ dataset show that our method significantly outperforms baseline methods.