# Combining 3D Model Contour Energy and Keypoints for Object Tracking

arXiv: https://arxiv.org/abs/2002.01379 · HTML: https://mlsanity.com/paper/2002.01379/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects](https://mlsanity.com/paper/2203.05334/) (2022)
- [SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World](https://mlsanity.com/paper/2110.12715/) (2021)
- [Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking](https://mlsanity.com/paper/2302.11458/) (2023)
- [Optical Flow-Guided 6DoF Object Pose Tracking with an Event Camera](https://mlsanity.com/paper/2512.21053/) (2025)
- [3D Keypoint Estimation Using Implicit Representation Learning](https://mlsanity.com/paper/2306.11529/) (2023)

## Abstract (arXiv)

We present a new combined approach for monocular model-based 3D tracking. A preliminary object pose is estimated by using a keypoint-based technique. The pose is then refined by optimizing the contour energy function. The energy determines the degree of correspondence between the contour of the model projection and the image edges. It is calculated based on both the intensity and orientation of the raw image gradient. For optimization, we propose a technique and search area constraints that allow overcoming the local optima and taking into account information obtained through keypoint-based pose estimation. Owing to its combined nature, our method eliminates numerous issues of keypoint-based and edge-based approaches. We demonstrate the efficiency of our method by comparing it with state-of-the-art methods on a public benchmark dataset that includes videos with various lighting conditions, movement patterns, and speed.