SGAligner : 3D Scene Alignment with Scene Graphs

• URL: http://arxiv.org/abs/2304.14880v2
• Date: Tue, 26 Sep 2023 22:21:06 GMT
• Title: SGAligner : 3D Scene Alignment with Scene Graphs
• Authors: Sayan Deb Sarkar, Ondrej Miksik, Marc Pollefeys, Daniel Barath, Iro Armeni
• Abstract summary: 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.
• Score: 84.01002998166145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building 3D scene graphs has recently emerged as a topic in scene representation for several embodied AI applications to represent the world in a structured and rich manner. With their increased use in solving downstream tasks (eg, navigation and room rearrangement), can we leverage and recycle them for creating 3D maps of environments, a pivotal step in agent operation? We focus on the fundamental problem of aligning pairs of 3D scene graphs whose overlap can range from zero to partial and can contain arbitrary changes. We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios (ie, unknown overlap -- if any -- and changes in the environment). We get inspired by multi-modality knowledge graphs and use contrastive learning to learn a joint, multi-modal embedding space. We evaluate on the 3RScan dataset and further showcase that our method can be used for estimating the transformation between pairs of 3D scenes. Since benchmarks for these tasks are missing, we create them on this dataset. The code, benchmark, and trained models are available on the project website.

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)
• Multiview Scene Graph [7.460438046915524]
  A proper scene representation is central to the pursuit of spatial intelligence. We propose to build Multiview Scene Graphs (MSG) from unposed images. MSG represents a scene topologically with interconnected place and object nodes.
  arXiv  Detail & Related papers  (2024-10-15T02:04:05Z)
• 3D Scene Diffusion Guidance using Scene Graphs [3.207455883863626]
  We propose a novel approach for 3D scene diffusion guidance using scene graphs. To leverage the relative spatial information the scene graphs provide, we make use of relational graph convolutional blocks within our denoising network.
  arXiv  Detail & Related papers  (2023-08-08T06:16:37Z)
• CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion [83.30168660888913]
  We present CommonScenes, a fully generative model that converts scene graphs into corresponding controllable 3D scenes. Our pipeline consists of two branches, one predicting the overall scene layout via a variational auto-encoder and the other generating compatible shapes. The generated scenes can be manipulated by editing the input scene graph and sampling the noise in the diffusion model.
  arXiv  Detail & Related papers  (2023-05-25T17:39:13Z)
• OpenScene: 3D Scene Understanding with Open Vocabularies [73.1411930820683]
  Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision. We propose OpenScene, an alternative approach where a model predicts dense features for 3D scene points that are co-embedded with text and image pixels in CLIP feature space. This zero-shot approach enables task-agnostic training and open-vocabulary queries.
  arXiv  Detail & Related papers  (2022-11-28T18:58:36Z)
• 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)
• Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions [94.17683799712397]
  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.
  arXiv  Detail & Related papers  (2020-04-08T12:25:25Z)

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.