Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships

CVPR 2024

Sebastian Koch1,2,3 *             Narunas Vaskevicius1,2            Mirco Colosi2
Pedro Hermosilla4              Timo Ropinski3
1Bosch Center for Artificial Intelligence   2Robert Bosch Corporate Research  
3University of Ulm   4Vienna University of Technology  

We present Open3DSG the first approach for learning to predict open-vocabulary 3D scene graphs from 3D point clouds. The advantage of our method is that it can be queried and prompted for any instance in the scene, such as the TV and Wall, to predict fine-grained semantic descriptions of objects and relationships.


Current approaches for 3D scene graph prediction rely on labeled datasets to train models for a fixed set of known object classes and relationship categories. 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. This enables us to predict 3D scene graphs from 3D point clouds in a zero-shot manner by querying object classes from an open vocabulary and predicting the interobject relationships from a grounded LLM with scene graph features and queried object classes as context.

Open3DSG is the first 3D point cloud method to predict not only explicit open-vocabulary object classes, but also open-set relationships that are not limited to a predefined label set, making it possible to express rare as well as specific objects and relationships in the predicted 3D scene graph. Our experiments show that Open3DSG is effective at predicting arbitrary object classes as well as their complex inter-object relationships describing spatial, supportive, semantic and comparative relationships.

Video 🎬

Method Overview

Given a point cloud and RGB-D images with their poses, we distill the knowledge of two vision-language models into our GNN. The nodes are supervised by the embedding of OpenSeg and the edges are supervised by the embedding of the InstructBLIP vision encoder. At inference time, we first compute the cosine similarity between object queries encoded by CLIP and our distilled 3D node features to infer the object classes. Then we use the edge embedding as well as the inferred object classes to predict relationships for pairs of objects using a Qformer & LLM from InstructBLIP.

Frame Selection

For each instance in the 3D point cloud, we select the top k frames for object and predicate supervision. For objects, we encode the frames using OpenSeg and aggregate the computed features over the projected points. For predicates, we identify object pairs in the frame, crop the image at multiple scales and compute the image feature with the BLIP image encoder. The features are aggregated over all crops. Finally, both object and predicate features are fused across the multiple views.

Scene Graph Predictions

We show the top-1 predictions on ScanNet from Open3DSG. The nodes are queried using the 3DSSG 160 class label set, while the edges are generated directly from the graph-conditioned LLM.

Open-Vocabulary 3D Scene Graph Applications

Object Retrieval using relationship description 3D Scene Graph + Open-Vocabulary Attributes

Reasoning over inter-object affordances by LLM prompting


      title={Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships},
      author={Koch, Sebastian  and Vaskevicius, Narunas and Colosi, Mirco and Hermosilla, Pedro and Ropinski, Timo},
      booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},