SAM3D-Part: Interactive Part Selection and Generation from 3D Objects

• URL: http://arxiv.org/abs/2609.15639v1
• Date: Mon, 14 Sep 2026 14:26:34 GMT
• Title: SAM3D-Part: Interactive Part Selection and Generation from 3D Objects
• Abstract summary: We present SAM3D-Part, a prompt-driven framework for selective part generation from input 3D object meshes.<n>Experiments and ablations demonstrate that SAM3D-Part can significantly improve source alignment, reduce conditioning cost, and enable consistent selective part generation.
• Score: 20.379932054770588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Part-level control is essential for modern 3D asset creation, where objects are frequently edited, reused, animated, or fabricated through their individual components. In many such workflows, users need only several specific components rather than a complete object decomposition. However, existing 3D generation methods produce all parts regardless of user intent, while promptable 3D segmentation methods typically output partial surfaces instead of reusable complete meshes. In addition, image-conditioned part generators further struggle to preserve hidden geometry and accurate placement without directly conditioning on the source mesh. To address these problems, we present SAM3D-Part, a prompt-driven framework for selective part generation from input 3D object meshes. Given a source mesh and a part prompt, SAM3D-Part first encodes the source geometry into compact mesh features and aligns them with the rendered image, selective mask, and point-map observations via pixel-wise channel fusion. The fused representation conditions a feed-forward generative model to produce only the queried component as a completed mesh. To place the generated part back into the source coordinate frame, SAM3D-Part predicts dense per-voxel correspondences and estimates the part transformation from distributed spatial evidence rather than a single global pose code. For sequential multi-part queries, previously generated parts are stored in a part cache and reused as contextual constraints, reducing conflicts among independently requested components. Extensive experiments and ablations demonstrate that SAM3D-Part can significantly improve source alignment, reduce conditioning cost, and enable consistent selective part generation, achieving state-of-the-art. Code and weights will be available at https://github.com/Jiahao620/sam3d-part.

Related papers

• MultiCube: Compositional 3D Generation With Part-Level Semantic and Spatial Control [43.67846683430802]
  MultiCube is a compositional 3D generation method that provides explicit, independent control over both the semantics and spatial arrangement of each part.<n>It outputs a 3D object composed of distinct meshes, one per specified part, that adhere to the given semantic and spatial conditions.
  arXiv  Detail & Related papers  (2026-08-20T17:44:48Z)
• SCULPT: Subtractive Composition for 3D Part Generation [48.947695076233835]
  Part-aware 3D generation aims to create digital assets that are coherent as complete objects.<n>We propose SCULPT, a framework that addresses these challenges through subtractive composition.
  arXiv  Detail & Related papers  (2026-08-13T17:55:04Z)
• AutoPartGen: Autogressive 3D Part Generation and Discovery [66.14255946353475]
  We introduce AutoPartGen, a model that generates objects composed of 3D parts in an autoregressive manner.<n>This model can take as input an image of an object, 2D masks of the object's parts, or an existing 3D object, and generate a corresponding compositional 3D reconstruction.<n>We evaluate both the overall 3D generation capabilities and the part-level generation quality of AutoPartGen, demonstrating that it achieves state-of-the-art performance in 3D part generation.
  arXiv  Detail & Related papers  (2025-07-17T17:59:47Z)
• PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers [29.52313100024294]
  We introduce PartCrafter, the first structured 3D generative model that jointly synthesizes multiple semantically meaningful and geometrically distinct 3D meshes from a single RGB image.<n>PartCrafter simultaneously denoises multiple 3D parts, enabling end-to-end part-aware generation of both individual objects and complex multi-object scenes.<n> Experiments show that PartCrafter outperforms existing approaches in generating decomposable 3D meshes.
  arXiv  Detail & Related papers  (2025-06-05T20:30:28Z)
• PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models [63.1432721793683]
  We introduce PartGen, a novel approach that generates 3D objects composed of meaningful parts starting from text, an image, or an unstructured 3D object.<n>We evaluate our method on generated and real 3D assets and show that it outperforms segmentation and part-extraction baselines by a large margin.
  arXiv  Detail & Related papers  (2024-12-24T18:59:43Z)
• Part123: Part-aware 3D Reconstruction from a Single-view Image [54.589723979757515]
  Part123 is a novel framework for part-aware 3D reconstruction from a single-view image. We introduce contrastive learning into a neural rendering framework to learn a part-aware feature space. A clustering-based algorithm is also developed to automatically derive 3D part segmentation results from the reconstructed models.
  arXiv  Detail & Related papers  (2024-05-27T07:10:21Z)
• Disentangled 3D Scene Generation with Layout Learning [109.03233745767062]
  We introduce a method to generate 3D scenes that are disentangled into their component objects. Our key insight is that objects can be discovered by finding parts of a 3D scene that, when rearranged spatially, still produce valid configurations of the same scene. We show that despite its simplicity, our approach successfully generates 3D scenes into individual objects.
  arXiv  Detail & Related papers  (2024-02-26T18:54:15Z)
• AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph [54.701098964773756]
  We aim to recover 3D objects with semantic parts and can be directly edited. Our work makes an attempt towards recovering two types of primitive-shaped objects, namely, generalized cuboids and generalized cylinders. Our algorithm can recover high quality 3D models and outperforms existing methods in both instance segmentation and 3D reconstruction.
  arXiv  Detail & Related papers  (2020-05-27T12:16:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.