# NeuralSVCD for Efficient Swept Volume Collision Detection

## TL;DR

NeuralSVCD introduces a neural network-based approach for swept volume collision detection that improves both accuracy and efficiency, enabling safer and faster robotic manipulation in complex environments.

## Contribution

The paper presents NeuralSVCD, a neural encoder-decoder architecture that balances efficiency and accuracy in SVCD by leveraging shape and temporal locality.

## Key findings

- Outperforms existing SVCD methods in accuracy
- Achieves higher computational efficiency
- Demonstrates robustness across various scenarios

## Abstract

Robot manipulation in unstructured environments requires efficient and reliable Swept Volume Collision Detection (SVCD) for safe motion planning. Traditional discrete methods potentially miss collisions between these points, whereas SVCD continuously checks for collisions along the entire trajectory. Existing SVCD methods typically face a trade-off between efficiency and accuracy, limiting practical use. In this paper, we introduce NeuralSVCD, a novel neural encoder-decoder architecture tailored to overcome this trade-off. Our approach leverages shape locality and temporal locality through distributed geometric representations and temporal optimization. This enhances computational efficiency without sacrificing accuracy. Comprehensive experiments show that NeuralSVCD consistently outperforms existing state-of-the-art SVCD methods in terms of both collision detection accuracy and computational efficiency, demonstrating its robust applicability across diverse robotic manipulation scenarios. Code and videos are available at https://neuralsvcd.github.io/.