RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval

• URL: http://arxiv.org/abs/2010.00973v1
• Date: Fri, 2 Oct 2020 13:06:12 GMT
• Title: RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval
• Authors: Rao Fu, Jie Yang, Jiawei Sun, Fang-Lue Zhang, Yu-Kun Lai and Lin Gao
• Abstract summary: Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class. We introduce a novel deep architecture, RISA-Net, which learns rotation invariant 3D shape descriptors. Our method is able to learn the importance of geometric and structural information of all the parts when generating the final compact latent feature of a 3D shape.
• Score: 46.02391761751015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be capable of representing detailed geometric information to discriminate shapes with globally similar structures. Moreover, 3D objects can be placed with arbitrary position and orientation in real-world applications, which further requires shape descriptors to be robust to rigid transformations. The shape descriptions used in existing 3D shape retrieval systems fail to meet the above two criteria. In this paper, we introduce a novel deep architecture, RISA-Net, which learns rotation invariant 3D shape descriptors that are capable of encoding fine-grained geometric information and structural information, and thus achieve accurate results on the task of fine-grained 3D object retrieval. RISA-Net extracts a set of compact and detailed geometric features part-wisely and discriminatively estimates the contribution of each semantic part to shape representation. Furthermore, our method is able to learn the importance of geometric and structural information of all the parts when generating the final compact latent feature of a 3D shape for fine-grained retrieval. We also build and publish a new 3D shape dataset with sub-class labels for validating the performance of fine-grained 3D shape retrieval methods. Qualitative and quantitative experiments show that our RISA-Net outperforms state-of-the-art methods on the fine-grained object retrieval task, demonstrating its capability in geometric detail extraction. The code and dataset are available at: https://github.com/IGLICT/RisaNET.

Related papers

• Geometry-guided Feature Learning and Fusion for Indoor Scene Reconstruction [14.225228781008209]
  This paper proposes a novel geometry integration mechanism for 3D scene reconstruction. Our approach incorporates 3D geometry at three levels, i.e. feature learning, feature fusion, and network supervision.
  arXiv  Detail & Related papers  (2024-08-28T08:02:47Z)
• 3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval [20.880210749809642]
  "geometric words" function as elemental constituents for representing entities through combinations. Each 3D or 2D entity can anchor its geometric terms within the knowledge graph, thereby serving as a link between cross-domain data. We evaluate the proposed method's performance on the ModelNet40 and ShapeNetCore55 datasets.
  arXiv  Detail & Related papers  (2022-10-27T02:51:24Z)
• 3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow [61.62796058294777]
  Reconstructing 3D shape from a single 2D image is a challenging task. Most of the previous methods still struggle to extract semantic attributes for 3D reconstruction task. We propose 3DAttriFlow to disentangle and extract semantic attributes through different semantic levels in the input images.
  arXiv  Detail & Related papers  (2022-03-29T02:03:31Z)
• Discovering 3D Parts from Image Collections [98.16987919686709]
  We tackle the problem of 3D part discovery from only 2D image collections. Instead of relying on manually annotated parts for supervision, we propose a self-supervised approach. Our key insight is to learn a novel part shape prior that allows each part to fit an object shape faithfully while constrained to have simple geometry.
  arXiv  Detail & Related papers  (2021-07-28T20:29:16Z)
• Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction [79.98689027127855]
  We propose a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects. Our method builds in a novel way on concepts from parametric deformation models, non-parametric 3D reconstruction, and canonical embeddings. It achieves state-of-the-art results in dense 3D reconstruction on public in-the-wild datasets of faces, cars, and birds.
  arXiv  Detail & Related papers  (2020-08-28T15:44:05Z)
• DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation [98.96086261213578]
  We introduce DSG-Net, a deep neural network that learns a disentangled structured and geometric mesh representation for 3D shapes. This supports a range of novel shape generation applications with disentangled control, such as of structure (geometry) while keeping geometry (structure) unchanged. Our method not only supports controllable generation applications but also produces high-quality synthesized shapes, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-12T17:06:51Z)
• STD-Net: Structure-preserving and Topology-adaptive Deformation Network for 3D Reconstruction from a Single Image [27.885717341244014]
  3D reconstruction from a single view image is a long-standing prob-lem in computer vision. In this paper, we propose a novel methodcalled STD-Net to reconstruct the 3D models utilizing the mesh representation. Experimental results on the images from ShapeNet show that ourproposed STD-Net has better performance than other state-of-the-art methods onreconstructing 3D objects.
  arXiv  Detail & Related papers  (2020-03-07T11:02:47Z)
• Unsupervised Learning of Intrinsic Structural Representation Points [50.92621061405056]
  Learning structures of 3D shapes is a fundamental problem in the field of computer graphics and geometry processing. We present a simple yet interpretable unsupervised method for learning a new structural representation in the form of 3D structure points.
  arXiv  Detail & Related papers  (2020-03-03T17:40:00Z)
• 3D Shape Segmentation with Geometric Deep Learning [2.512827436728378]
  We propose a neural-network based approach that produces 3D augmented views of the 3D shape to solve the whole segmentation as sub-segmentation problems. We validate our approach using 3D shapes of publicly available datasets and of real objects that are reconstructed using photogrammetry techniques.
  arXiv  Detail & Related papers  (2020-02-02T14:11:16Z)

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.