3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval

• URL: http://arxiv.org/abs/2210.15136v2
• Date: Thu, 21 Dec 2023 11:31:38 GMT
• Title: 3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval
• Authors: Rihao Chang, Yongtao Ma, Tong Hao, Weizhi Nie
• Abstract summary: "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.
• Score: 20.880210749809642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The surge in 3D modeling has led to a pronounced research emphasis on the field of 3D shape retrieval. Numerous contemporary approaches have been put forth to tackle this intricate challenge. Nevertheless, effectively addressing the intricacies of cross-modal 3D shape retrieval remains a formidable undertaking, owing to inherent modality-based disparities. This study presents an innovative notion, termed "geometric words", which functions as elemental constituents for representing entities through combinations. To establish the knowledge graph, we employ geometric words as nodes, connecting them via shape categories and geometry attributes. Subsequently, we devise a unique graph embedding method for knowledge acquisition. Finally, an effective similarity measure is introduced for retrieval purposes. Importantly, each 3D or 2D entity can anchor its geometric terms within the knowledge graph, thereby serving as a link between cross-domain data. As a result, our approach facilitates multiple cross-domain 3D shape retrieval tasks. We evaluate the proposed method's performance on the ModelNet40 and ShapeNetCore55 datasets, encompassing scenarios related to 3D shape retrieval and cross-domain retrieval. Furthermore, we employ the established cross-modal dataset (MI3DOR) to assess cross-modal 3D shape retrieval. The resulting experimental outcomes, in conjunction with comparisons against state-of-the-art techniques, clearly highlight the superiority of our approach.

Related papers

• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• COM3D: Leveraging Cross-View Correspondence and Cross-Modal Mining for 3D Retrieval [21.070154402838906]
  We propose COM3D, making the first attempt to exploit the cross-view correspondence and cross-modal mining to enhance the retrieval performance. Notably, we augment the 3D features through a scene representation transformer, to generate cross-view correspondence features of 3D shapes. Furthermore, we propose to optimize the cross-modal matching process based on the semi-hard negative example mining method.
  arXiv  Detail & Related papers  (2024-05-07T08:16:13Z)
• 3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations [81.45521258652734]
  We propose a method to create plausible geometric and texture style variations of 3D objects. Our method can create many novel stylized shapes, resulting in effortless 3D content creation and style-ware data augmentation.
  arXiv  Detail & Related papers  (2021-08-30T02:28:31Z)
• Zero in on Shape: A Generic 2D-3D Instance Similarity Metric learned from Synthetic Data [3.71630298053787]
  We present a network architecture which compares RGB images and untextured 3D models by the similarity of the represented shape. Our system is optimised for zero-shot retrieval, meaning it can recognise shapes never shown in training.
  arXiv  Detail & Related papers  (2021-08-09T14:44:08Z)
• Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections [57.60094385551773]
  We propose a trainable framework for learning a deformable 3D geometry model from inhomogeneous image collections. We in addition obtain the underlying 3D geometry of the objects depicted in the 2D images.
  arXiv  Detail & Related papers  (2021-03-31T17:25:36Z)
• Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning [97.56893524594703]
  Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database. metric learning with some adaptation techniques seems to be a natural solution to shape similarity learning. We develop a geometry-focused multi-view metric learning framework empowered by texture synthesis.
  arXiv  Detail & Related papers  (2020-10-23T08:52:00Z)
• RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval [46.02391761751015]
  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.
  arXiv  Detail & Related papers  (2020-10-02T13:06:12Z)
• Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning [8.448611728105513]
  We propose to extend the InfoMax and contrastive learning principles on 3D shapes. We show that we can maximize the mutual information between 3D objects and their "chunks" to improve the representations in aligned datasets.
  arXiv  Detail & Related papers  (2020-06-04T00:30:26Z)
• DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes [54.239416488865565]
  We propose a fast single-stage 3D object detection method for LIDAR data. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. We find that our proposed method achieves state-of-the-art results by 5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Open dataset.
  arXiv  Detail & Related papers  (2020-04-02T17:48:50Z)
• Learning 3D Human Shape and Pose from Dense Body Parts [117.46290013548533]
  We propose a Decompose-and-aggregate Network (DaNet) to learn 3D human shape and pose from dense correspondences of body parts. Messages from local streams are aggregated to enhance the robust prediction of the rotation-based poses. Our method is validated on both indoor and real-world datasets including Human3.6M, UP3D, COCO, and 3DPW.
  arXiv  Detail & Related papers  (2019-12-31T15:09:51Z)

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.