# A Non-linear Differential CNN-Rendering Module for 3D Data Enhancement

## TL;DR

This paper introduces a learnable, non-linear differential rendering module for neural networks that enhances processing of cluttered 3D data by focusing on important regions and bypassing occlusions and noise.

## Contribution

The work presents a novel differentiable rendering module with sensor cells that adaptively focus on relevant data regions, improving neural network performance on cluttered 3D data.

## Key findings

- Improves classification accuracy in cluttered environments
- Efficiently handles occlusions and non-linear deformations
- Enhances localization in 2D and 3D data

## Abstract

In this work we introduce a differential rendering module which allows neural networks to efficiently process cluttered data. The module is composed of continuous piecewise differentiable functions defined as a sensor array of cells embedded in 3D space. Our module is learnable and can be easily integrated into neural networks allowing to optimize data rendering towards specific learning tasks using gradient based methods in an end-to-end fashion. Essentially, the module's sensor cells are allowed to transform independently and locally focus and sense different parts of the 3D data. Thus, through their optimization process, cells learn to focus on important parts of the data, bypassing occlusions, clutter and noise. Since sensor cells originally lie on a grid, this equals to a highly non-linear rendering of the scene into a 2D image. Our module performs especially well in presence of clutter and occlusions. Similarly, it deals well with non-linear deformations and improves classification accuracy through proper rendering of the data. In our experiments, we apply our module to demonstrate efficient localization and classification tasks in cluttered data both 2D and 3D.