Assembler: Scalable 3D Part Assembly via Anchor Point Diffusion (2506.17074v1)

Abstract: We present Assembler, a scalable and generalizable framework for 3D part assembly that reconstructs complete objects from input part meshes and a reference image. Unlike prior approaches that mostly rely on deterministic part pose prediction and category-specific training, Assembler is designed to handle diverse, in-the-wild objects with varying part counts, geometries, and structures. It addresses the core challenges of scaling to general 3D part assembly through innovations in task formulation, representation, and data. First, Assembler casts part assembly as a generative problem and employs diffusion models to sample plausible configurations, effectively capturing ambiguities arising from symmetry, repeated parts, and multiple valid assemblies. Second, we introduce a novel shape-centric representation based on sparse anchor point clouds, enabling scalable generation in Euclidean space rather than SE(3) pose prediction. Third, we construct a large-scale dataset of over 320K diverse part-object assemblies using a synthesis and filtering pipeline built on existing 3D shape repositories. Assembler achieves state-of-the-art performance on PartNet and is the first to demonstrate high-quality assembly for complex, real-world objects. Based on Assembler, we further introduce an interesting part-aware 3D modeling system that generates high-resolution, editable objects from images, demonstrating potential for interactive and compositional design. Project page: https://assembler3d.github.io

An Expert Overview of "Assembler: Scalable 3D Part Assembly via Anchor Point Diffusion"

The paper "Assembler: Scalable 3D Part Assembly via Anchor Point Diffusion" introduces a novel framework for reconstructing complete 3D objects from modular parts in a scalable manner. The Assembler departs from traditional deterministic, category-specific methods by employing generative diffusion models and anchor point-based shape representation, enhancing its applicability to diverse and complex real-world objects. This overview aims to elucidate the methodology, results, and future implications presented in the research, outlining its contributions to the field of computer vision and graphics.

The researchers position 3D part assembly as a generative problem, leveraging diffusion models to prospectively sample several plausible part configurations. This paradigm shift is crucial for addressing symmetry, repeated components, and multiple valid object reconstructions that are characteristic of in-the-wild 3D objects. The introduction of a shape-centric representation based on sparse anchor point clouds facilitates scalable modeling directly in Euclidean space, diverging from the conventional SE(3) pose prediction approaches. The use of sparse anchor points not only simplifies the generation process but ensures flexibility and generalizability across various objects with different part counts and geometries. Such adaptability is paramount for achieving the broad application scope originally envisioned for automated 3D assembly systems.

Significantly, the authors acknowledge the lack of an existing large-scale dataset suitable for training generalizable models for 3D part assembly. To address this, they present a robust data synthesis and filtering pipeline, which assembles over 320,000 part-object pairings from existing 3D repositories. This dataset exemplifies the scalability required for real-world applications, enhancing the model's capacity to handle diverse object categories with high variability in part count and structural complexity. Quantitative and qualitative evaluations highlight Assembler's superior performance on benchmarks like PartNet, confirming its efficacy in generalizing to novel and complex object forms.

The implications of this research are manifold. Practically, Assembler enhances 3D modeling workflows by providing a system that can automatically and accurately piece together complex models, which could revolutionize computer-aided design, manufacturing, and robotics. Theoretically, the paper lays a foundation for future explorations into generative approaches for other assembly problems, encouraging the adoption of diffusion models for tasks beyond the static prediction frameworks currently dominating the field.

However, challenges remain. While the paper demonstrates high accuracy in many scenarios, there are noted difficulties with extremely intricate or small components, presenting opportunities for future research. Enhancements could involve integrating larger-scale diffusion models or more advanced data-driven techniques, potentially even incorporating unsupervised learning paradigms to refine assembly prediction accuracy and reliability further.

In conclusion, "Assembler: Scalable 3D Part Assembly via Anchor Point Diffusion" constitutes a pivotal advancement in automated 3D assembly research, showcasing the potential of generative models for scalable and generalizable system development. By combining innovative problem formulations with robust data representations, Assembler provides a comprehensive template for the automated assembly of 3D objects, heralding significant contributions to both practical applications and theoretical frameworks within the AI and 3D modeling disciplines.