# Representation Based Regression for Object Distance Estimation

arXiv: https://arxiv.org/abs/2106.14208 · HTML: https://mlsanity.com/paper/2106.14208/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 27 Jun 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://mlsanity.com/paper/1802.05522/) (2018)
- [Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos](https://mlsanity.com/paper/1811.06152/) (2018)
- [Learning Object-specific Distance from a Monocular Image](https://mlsanity.com/paper/1909.04182/) (2019)
- [Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images](https://mlsanity.com/paper/2005.04014/) (2020)
- [Plug-and-Play: Improve Depth Estimation via Sparse Data Propagation](https://mlsanity.com/paper/1812.08350/) (2018)
- [Advance Warning Methodologies for COVID-19 using Chest X-Ray Images](https://mlsanity.com/paper/2006.05332/) (2020)
- [Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?](https://mlsanity.com/paper/1806.04329/) (2018)
- [Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing](https://mlsanity.com/paper/2003.00768/) (2020)

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

- [MS23D: A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer](https://mlsanity.com/paper/2308.16518/) (2023)
- [Operational Support Estimator Networks](https://mlsanity.com/paper/2307.06065/) (2023)
- [Unmasking Airborne Threats: Guided-Transformers for Portable Aerosol Mass Spectrometry](https://mlsanity.com/paper/2511.17446/) (2025)

## Abstract (arXiv)

In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional Support Estimator Networks (CSENs). CSENs are designed to compute a direct mapping for the Support Estimation (SE) task in a representation-based classification problem. We further propose and demonstrate that representation-based methods (sparse or collaborative representation) can be used in well-designed regression problems. To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR). The initial version of CSENs has a proxy mapping stage (i.e., a coarse estimation for the support set) that is required for the input. In this study, we improve the CSEN model by proposing Compressive Learning CSEN (CL-CSEN) that has the ability to jointly optimize the so-called proxy mapping stage along with convolutional layers. The experimental evaluations using the KITTI 3D Object Detection distance estimation dataset show that the proposed method can achieve a significantly improved distance estimation performance over all competing methods. Finally, the software implementations of the methods are publicly shared at https://github.com/meteahishali/CSENDistance.