Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions

• URL: http://arxiv.org/abs/2004.03967v1
• Date: Wed, 8 Apr 2020 12:25:25 GMT
• Title: Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions
• Authors: Johanna Wald, Helisa Dhamo, Nassir Navab, Federico Tombari
• Abstract summary: We focus on scene graphs, a data structure that organizes the entities of a scene in a graph. We propose a learned method that regresses a scene graph from the point cloud of a scene. We show the application of our method in a domain-agnostic retrieval task, where graphs serve as an intermediate representation for 3D-3D and 2D-3D matching.
• Score: 94.17683799712397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene understanding has been of high interest in computer vision. It encompasses not only identifying objects in a scene, but also their relationships within the given context. With this goal, a recent line of works tackles 3D semantic segmentation and scene layout prediction. In our work we focus on scene graphs, a data structure that organizes the entities of a scene in a graph, where objects are nodes and their relationships modeled as edges. We leverage inference on scene graphs as a way to carry out 3D scene understanding, mapping objects and their relationships. In particular, we propose a learned method that regresses a scene graph from the point cloud of a scene. Our novel architecture is based on PointNet and Graph Convolutional Networks (GCN). In addition, we introduce 3DSSG, a semi-automatically generated dataset, that contains semantically rich scene graphs of 3D scenes. We show the application of our method in a domain-agnostic retrieval task, where graphs serve as an intermediate representation for 3D-3D and 2D-3D matching.

Related papers

• Open-Vocabulary Octree-Graph for 3D Scene Understanding [54.11828083068082]
  Octree-Graph is a novel scene representation for open-vocabulary 3D scene understanding. An adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape.
  arXiv  Detail & Related papers  (2024-11-25T10:14:10Z)
• Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships [15.513180297629546]
  We present Open3DSG, an alternative approach to learn 3D scene graph prediction in an open world without requiring labeled scene graph data. We co-embed the features from a 3D scene graph prediction backbone with the feature space of powerful open world 2D vision language foundation models.
  arXiv  Detail & Related papers  (2024-02-19T16:15:03Z)
• Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction [16.643252717745348]
  We present the first language-based pre-training approach for 3D scene graphs. We leverage the language encoder of CLIP, a popular vision-language model, to distill its knowledge into our graph-based network. Our method achieves state-of-the-art results on the main semantic 3D scene graph benchmark.
  arXiv  Detail & Related papers  (2023-10-25T09:26:16Z)
• ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning [125.90002884194838]
  ConceptGraphs is an open-vocabulary graph-structured representation for 3D scenes. It is built by leveraging 2D foundation models and fusing their output to 3D by multi-view association. We demonstrate the utility of this representation through a number of downstream planning tasks.
  arXiv  Detail & Related papers  (2023-09-28T17:53:38Z)
• SGAligner : 3D Scene Alignment with Scene Graphs [84.01002998166145]
  Building 3D scene graphs has emerged as a topic in scene representation for several embodied AI applications. We focus on the fundamental problem of aligning pairs of 3D scene graphs whose overlap can range from zero to partial. We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios.
  arXiv  Detail & Related papers  (2023-04-28T14:39:22Z)
• Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs [85.54212143154986]
  Controllable scene synthesis consists of generating 3D information that satisfy underlying specifications. Scene graphs are representations of a scene composed of objects (nodes) and inter-object relationships (edges) We propose the first work that directly generates shapes from a scene graph in an end-to-end manner.
  arXiv  Detail & Related papers  (2021-08-19T17:59:07Z)
• SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences [76.28527350263012]
  We propose a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames. We aggregate PointNet features from primitive scene components by means of a graph neural network. Our approach outperforms 3D scene graph prediction methods by a large margin and its accuracy is on par with other 3D semantic and panoptic segmentation methods while running at 35 Hz.
  arXiv  Detail & Related papers  (2021-03-27T13:00:36Z)

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.