Point2Graph: An End-to-end Point Cloud-based 3D Open-Vocabulary Scene Graph for Robot Navigation

• URL: http://arxiv.org/abs/2409.10350v1
• Date: Mon, 16 Sep 2024 15:01:28 GMT
• Title: Point2Graph: An End-to-end Point Cloud-based 3D Open-Vocabulary Scene Graph for Robot Navigation
• Abstract summary: Point2Graph is a novel end-to-end point cloud-based 3D open-vocabulary scene graph generation framework. For the room layer, we leverage the advantage of merging the geometry-based border detection algorithm with the learning-based region detection to segment rooms. In addition, we create an end-to-end pipeline for the object layer to detect and classify 3D objects based solely on 3D point cloud data.
• Score: 7.048930431483034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current open-vocabulary scene graph generation algorithms highly rely on both 3D scene point cloud data and posed RGB-D images and thus have limited applications in scenarios where RGB-D images or camera poses are not readily available. To solve this problem, we propose Point2Graph, a novel end-to-end point cloud-based 3D open-vocabulary scene graph generation framework in which the requirement of posed RGB-D image series is eliminated. This hierarchical framework contains room and object detection/segmentation and open-vocabulary classification. For the room layer, we leverage the advantage of merging the geometry-based border detection algorithm with the learning-based region detection to segment rooms and create a "Snap-Lookup" framework for open-vocabulary room classification. In addition, we create an end-to-end pipeline for the object layer to detect and classify 3D objects based solely on 3D point cloud data. Our evaluation results show that our framework can outperform the current state-of-the-art (SOTA) open-vocabulary object and room segmentation and classification algorithm on widely used real-scene datasets.

Related papers

• SceneGraphGrounder: Zero-Shot 3D Visual Grounding via Structured Scene Graph Matching [2.4737219202679808]
  We propose SceneGrapher, a framework that reformulates 3D grounding as structured graph matching over a reconstructed 3D scene graph.<n>We demonstrate our framework through real-world deployment on a mobile robot, demonstrating robust spatial reasoning in long-horizon physical environments.
  arXiv  Detail & Related papers  (2026-05-20T22:30:51Z)
• 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)
• SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks [14.548198408544032]
  We treat 3D scene graph alignment as a partial graph-matching problem and propose to solve it with a graph neural network. We reuse the geometric features learned by a point cloud registration method and associate the clustered point-level geometric features with the node-level semantic feature. We propose a point-matching rescoring method, that uses the node-wise alignment of the 3D scene graph to reweight the matching candidates from a pre-trained point cloud registration method.
  arXiv  Detail & Related papers  (2024-03-28T15:01:58Z)
• ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models [77.84408427496025]
  State-of-the-art 3D point cloud registration methods rely on labeled 3D datasets for training. We introduce ZeroReg, a zero-shot registration approach that utilizes 2D foundation models to predict 3D correspondences.
  arXiv  Detail & Related papers  (2023-12-05T11:33:16Z)
• Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection [24.871590175483096]
  Point cloud-based open-vocabulary 3D object detection aims to detect 3D categories that do not have ground-truth annotations in the training set. Previous approaches leverage large-scale richly-annotated image datasets as a bridge between 3D and category semantics. We propose Object2Scene, the first approach that leverages large-scale large-vocabulary 3D object datasets to augment existing 3D scene datasets for open-vocabulary 3D object detection.
  arXiv  Detail & Related papers  (2023-09-18T03:31:53Z)
• Open-Vocabulary Point-Cloud Object Detection without 3D Annotation [62.18197846270103]
  The goal of open-vocabulary 3D point-cloud detection is to identify novel objects based on arbitrary textual descriptions. We develop a point-cloud detector that can learn a general representation for localizing various objects. We also propose a novel de-biased triplet cross-modal contrastive learning to connect the modalities of image, point-cloud and text.
  arXiv  Detail & Related papers  (2023-04-03T08:22:02Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• RBGNet: Ray-based Grouping for 3D Object Detection [104.98776095895641]
  We propose the RBGNet framework, a voting-based 3D detector for accurate 3D object detection from point clouds. We propose a ray-based feature grouping module, which aggregates the point-wise features on object surfaces using a group of determined rays. Our model achieves state-of-the-art 3D detection performance on ScanNet V2 and SUN RGB-D with remarkable performance gains.
  arXiv  Detail & Related papers  (2022-04-05T14:42:57Z)
• ParaNet: Deep Regular Representation for 3D Point Clouds [62.81379889095186]
  ParaNet is a novel end-to-end deep learning framework for representing 3D point clouds. It converts an irregular 3D point cloud into a regular 2D color image, named point geometry image (PGI) In contrast to conventional regular representation modalities based on multi-view projection and voxelization, the proposed representation is differentiable and reversible.
  arXiv  Detail & Related papers  (2020-12-05T13:19:55Z)
• An Overview Of 3D Object Detection [21.159668390764832]
  We propose a framework that uses both RGB and point cloud data to perform multiclass object recognition. We use the recently released nuScenes dataset---a large-scale dataset contains many data formats---to training and evaluate our proposed architecture.
  arXiv  Detail & Related papers  (2020-10-29T14:04:50Z)
• 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.