TABI: Tight and Balanced Interactive Atlas Packing

2026-02-08

AI Summary

This work proposes a GPU-accelerated interactive atlas packing method that bridges the gap between real-time performance and high-quality offline approaches. Existing real-time techniques often suffer from excessive chart scaling, while offline methods fail to meet the latency requirements of interactive applications. The proposed method achieves near-offline packing quality in real time by enabling tight layout with balanced arrangement—eliminating inter-chart gaps, adaptively adjusting row widths and orientations, and applying bidirectional (horizontal and vertical) gap compression. It further incorporates two efficient shape approximation models to enhance both packing efficiency and visual fidelity. Experimental results demonstrate that, compared to current interactive methods, the approach significantly reduces scaling artifacts, attains packing quality comparable to offline algorithms, and achieves over two orders of magnitude speedup in runtime performance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Matrix & Tensor Methods
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

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.

Problem

• atlas packing
• interactive performance
• chart downscaling
• packing quality
• real-time GPU

Innovation

• atlas packing
• interactive performance
• GPU acceleration
• tight packing
• balanced layout