TLDR: Light-SQ is a new framework that converts complex, generated 3D meshes into simpler, editable representations using flexible geometric shapes called superquadrics. It achieves this by preventing primitive overlap, aligning primitives with the object’s natural structure, and ensuring a compact representation. Evaluated on a new benchmark called 3DGen-Prim, Light-SQ significantly improves both the accuracy and editability of 3D models compared to previous methods, making 3D content creation more accessible for non-experts.
Creating 3D assets for user-generated content (UGC) platforms often involves non-expert users relying on advanced image-to-3D generative models. While these models have made it easier to produce high-quality 3D meshes from simple images, the resulting models are frequently overly detailed, disorganized, and challenging to edit. This complexity poses significant hurdles for tasks like animation, rigging, and interactive content creation.
A promising solution to these challenges is primitive-based shape abstraction. This technique converts bulky, high-resolution 3D models into a compact set of simpler, editable geometric shapes, known as primitives. This not only drastically reduces storage size but also makes the models much easier to manipulate. However, existing abstraction methods often struggle with the irregularities and noise common in generated 3D geometry.
Introducing Light-SQ: A New Approach to 3D Shape Abstraction
Researchers have developed Light-SQ, a novel framework designed specifically for abstracting generated 3D meshes using superquadrics. Superquadrics are versatile geometric primitives that can represent a wide range of shapes, from spheres and cubes to more complex forms, with just a few parameters. Light-SQ’s core innovation lies in its “structure-aware” approach, which ensures that the abstracted shapes are not only accurate but also easy to edit and understand.
The framework addresses three key aspects of structure-awareness:
- Low Overlap: Primitives should occupy distinct spatial regions with minimal overlap.
- Part-Aware Alignment: Primitives should align with the natural structural parts of the object.
- Compactness: The representation should avoid excessive fragmentation, making it easier for downstream tasks.
How Light-SQ Achieves Structure-Aware Abstraction
Light-SQ incorporates several innovative components to achieve its goals:
SDF Carving: This technique iteratively updates the target Signed Distance Field (SDF), a representation of the 3D shape, to prevent newly fitted superquadrics from overlapping with existing ones. By effectively “carving out” regions already covered, Light-SQ ensures that each primitive has a clear, distinct space, which is crucial for editability.
Structure-Aware Alignment with Block-Regrow-Fill: Instead of relying on potentially noisy semantic segmentation, Light-SQ uses geometrical analysis to guide its abstraction. It first breaks down the 3D shape into meaningful convex parts. Then, a three-phase “block-regrow-fill” strategy is employed. In the “block” stage, a few superquadrics are fitted to each part. The “regrow” stage allows these primitives to expand and fill gaps, forming flexible boundaries. Finally, the “fill” stage adds more superquadrics to cover any remaining under-fitted regions. This process ensures that primitives align well with the object’s inherent structure.
Adaptive Residual Pruning: To maintain a compact representation and avoid over-segmentation, Light-SQ tracks the history of SDF updates. This allows it to classify residual primitives based on their geometric significance. Small, meaningful components (like a plane’s propeller) are retained, while insignificant fragments (like noise from fitting a slightly imperfect sphere) are discarded. This ensures that the final abstraction is clean and focused on essential details.
Multiscale Fitting: Light-SQ also supports multiscale fitting, meaning it can refine coarse primitives to capture finer geometric details. Users can upsample specific regions to achieve higher precision where needed, offering a flexible balance between abstraction detail and reconstruction quality.
Evaluating Light-SQ with 3DGen-Prim
To rigorously evaluate Light-SQ, the researchers introduced 3DGen-Prim, a new benchmark dataset. This dataset extends 3DGen-Bench with outputs from state-of-the-art image-to-3D generative models, providing a challenging environment for testing abstraction methods on real-world generated geometry. 3DGen-Prim includes new metrics to assess both how accurately the abstraction fits the original shape (fidelity) and how easy it is to edit (editability).
Also Read:
- UniLat3D: A Single-Stage Approach to High-Quality 3D Asset Generation
- SIMECO: A New Approach to 3D Shape Completion That Learns Intrinsic Geometry
Impressive Results and User Validation
Extensive experiments demonstrated that Light-SQ significantly outperforms existing optimization-based, learning-based, and rule-based methods. It achieves high-fidelity reconstruction of complex generated geometry while simultaneously ensuring very low primitive overlap and strong structure-aware alignment. User studies further confirmed Light-SQ’s advantages in geometry editability, editing efficiency, texture editability, texture editing efficiency, and animation friendliness. Moreover, Light-SQ proved to be computationally efficient, completing abstraction significantly faster than many comparable methods.
In conclusion, Light-SQ represents a significant step forward in 3D shape abstraction for user-generated content. By providing compact, editable, and accurate representations of complex generated meshes, it enhances the feasibility of 3D UGC creation and opens new possibilities for interactive 3D applications.
