TABI: Tight and Balanced Interactive Atlas Packing

TL;DR

TABI is a GPU-based interactive atlas packing algorithm that produces high-quality, tightly packed, and balanced chart arrangements, significantly reducing downscaling artifacts while maintaining real-time performance.

Contribution

The paper introduces a novel GPU packing method that achieves near-offline packing quality in real-time, with automatic balancing and artifact elimination.

Findings

Reduces chart downscaling compared to existing interactive methods

Produces tightly packed and balanced chart arrangements

Operates at interactive speeds suitable for real-time applications

Abstract

Atlas packing is a key step in many computer graphics applications. Packing algorithms seek to arrange a set of charts within a fixed-size atlas with as little downscaling as possible. Many packing applications such as content creation tools, dynamic atlas generation for video games, and texture space shading require on-the-fly interactive atlas packing. Unfortunately, while many methods have been developed for generating tight high-quality packings, they are designed for offline settings and have running times two or more orders of magnitude greater than what is required for interactive performance. While real-time GPU packing methods exist, they significantly downscale packed charts compared to offline methods. We introduce a GPU packing method that targets interactive speeds, provides packing quality approaching that of offline methods, and supports flexible user control over the tradeoff between performance and quality. We observe that current real-time packing methods leave large gaps between charts and often produce asymmetric, or poorly balanced, packings. These artifacts dramatically degrade packing quality. Our Tight And Balanced method eliminates these artifacts while retaining Interactive performance. TABI generates tight packings by compacting empty space between irregularly shaped charts both horizontally and vertically, using two approximations of chart shape that support efficient parallel processing. We balance packing outputs by automatically adjusting atlas row widths and orientations to accommodate varying chart heights. We show that our method significantly reduces chart downscaling compared to existing interactive methods while remaining orders of magnitude faster than offline alternatives.