Compressed Bounding Volume Hierarchies for Collision Detection & Proximity Query

TL;DR

This paper introduces a new compressed BVH data structure that enhances collision detection and proximity queries by fitting into CPU cache, maintaining fast access, and reducing memory usage.

Contribution

It proposes a novel compression scheme for BVH descriptors and a cache-aware clustering method to improve performance in collision detection tasks.

Findings

Reduced memory footprint of BVH structures

Improved cache efficiency during traversal

Maintained fast random access for queries

Abstract

We present a novel representation of compressed data structure for simultaneous bounding volume hierarchy (BVH) traversals like they appear for instance in collision detection & proximity query. The main idea is to compress bounding volume (BV) descriptors and cluster BVH into a smaller parts 'treelet' that fit into CPU cache while at the same time maintain random-access and automatic cache-aware data structure layouts. To do that, we quantify BV and compress 'treelet' using predictor-corrector scheme with the predictor at a specific node in the BVH based on the chain of BVs upwards.