# See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG

arXiv: https://arxiv.org/abs/1505.00663 · HTML: https://mlsanity.com/paper/1505.00663/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2015.

## Built on by (most cited first, 1 of 1)

- [Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module](https://mlsanity.com/paper/2309.00752/) (2023)

## Abstract (arXiv)

The Histogram of Oriented Gradient (HOG) descriptor has led to many advances in computer vision over the last decade and is still part of many state of the art approaches. We realize that the associated feature computation is piecewise differentiable and therefore many pipelines which build on HOG can be made differentiable. This lends to advanced introspection as well as opportunities for end-to-end optimization. We present our implementation of nablaHOG based on the auto-differentiation toolbox Chumpy and show applications to pre-image visualization and pose estimation which extends the existing differentiable renderer OpenDR pipeline. Both applications improve on the respective state-of-the-art HOG approaches.