Articulate3D: Holistic Understanding of 3D Scenes as Universal Scene Description

• URL: http://arxiv.org/abs/2412.01398v2
• Date: Wed, 02 Jul 2025 12:33:12 GMT
• Title: Articulate3D: Holistic Understanding of 3D Scenes as Universal Scene Description
• Authors: Anna-Maria Halacheva, Yang Miao, Jan-Nico Zaech, Xi Wang, Luc Van Gool, Danda Pani Paudel,
• Abstract summary: 3D scene understanding is a long-standing challenge in computer vision and a key component in enabling mixed reality, wearable computing, and embodied AI.<n>We introduce Articulate3D, an expertly curated 3D dataset featuring high-quality manual annotations on 280 indoor scenes.<n>We also present USDNet, a novel unified framework capable of simultaneously predicting part segmentation along with a full specification of motion attributes for articulated objects.
• Score: 56.69740649781989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D scene understanding is a long-standing challenge in computer vision and a key component in enabling mixed reality, wearable computing, and embodied AI. Providing a solution to these applications requires a multifaceted approach that covers scene-centric, object-centric, as well as interaction-centric capabilities. While there exist numerous datasets and algorithms approaching the former two problems, the task of understanding interactable and articulated objects is underrepresented and only partly covered in the research field. In this work, we address this shortcoming by introducing: (1) Articulate3D, an expertly curated 3D dataset featuring high-quality manual annotations on 280 indoor scenes. Articulate3D provides 8 types of annotations for articulated objects, covering parts and detailed motion information, all stored in a standardized scene representation format designed for scalable 3D content creation, exchange and seamless integration into simulation environments. (2) USDNet, a novel unified framework capable of simultaneously predicting part segmentation along with a full specification of motion attributes for articulated objects. We evaluate USDNet on Articulate3D as well as two existing datasets, demonstrating the advantage of our unified dense prediction approach. Furthermore, we highlight the value of Articulate3D through cross-dataset and cross-domain evaluations and showcase its applicability in downstream tasks such as scene editing through LLM prompting and robotic policy training for articulated object manipulation. We provide open access to our dataset, benchmark, and method's source code.

Related papers

• Segment Any 3D-Part in a Scene from a Sentence [50.46950922754459]
  This paper aims to achieve the segmentation of any 3D part in a scene based on natural language descriptions.<n>We introduce the 3D-PU dataset, the first large-scale 3D dataset with dense part annotations.<n>On the methodological side, we propose OpenPart3D, a 3D-input-only framework to tackle the challenges of part-level segmentation.
  arXiv  Detail & Related papers  (2025-06-24T05:51:22Z)
• IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments [56.85804719947]
  We present IAAO, a framework that builds an explicit 3D model for intelligent agents to gain understanding of articulated objects in their environment through interaction. We first build hierarchical features and label fields for each object state using 3D Gaussian Splatting (3DGS) by distilling mask features and view-consistent labels from multi-view images. We then perform object- and part-level queries on the 3D Gaussian primitives to identify static and articulated elements, estimating global transformations and local articulation parameters along with affordances.
  arXiv  Detail & Related papers  (2025-04-09T12:36:48Z)
• CrossOver: 3D Scene Cross-Modal Alignment [78.3057713547313]
  CrossOver is a novel framework for cross-modal 3D scene understanding. It learns a unified, modality-agnostic embedding space for scenes by aligning modalities. It supports robust scene retrieval and object localization, even with missing modalities.
  arXiv  Detail & Related papers  (2025-02-20T20:05:30Z)
• MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations [55.022519020409405]
  This paper builds the first largest ever multi-modal 3D scene dataset and benchmark with hierarchical grounded language annotations, MMScan.<n>The resulting multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding and question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-06-13T17:59:30Z)
• Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers [65.51132104404051]
  We introduce the use of object identifiers and object-centric representations to interact with scenes at the object level. Our model significantly outperforms existing methods on benchmarks including ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.
  arXiv  Detail & Related papers  (2023-12-13T14:27:45Z)
• ROAM: Robust and Object-Aware Motion Generation Using Neural Pose Descriptors [73.26004792375556]
  This paper shows that robustness and generalisation to novel scene objects in 3D object-aware character synthesis can be achieved by training a motion model with as few as one reference object. We leverage an implicit feature representation trained on object-only datasets, which encodes an SE(3)-equivariant descriptor field around the object. We demonstrate substantial improvements in 3D virtual character motion and interaction quality and robustness to scenarios with unseen objects.
  arXiv  Detail & Related papers  (2023-08-24T17:59:51Z)
• AutoDecoding Latent 3D Diffusion Models [95.7279510847827]
  We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core. The 3D autodecoder framework embeds properties learned from the target dataset in the latent space. We then identify the appropriate intermediate volumetric latent space, and introduce robust normalization and de-normalization operations.
  arXiv  Detail & Related papers  (2023-07-07T17:59:14Z)
• CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection [57.44434974289945]
  We propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework. Our framework takes a 3D scene as input and strives to explicitly integrate useful contextual information of the scene. In addition to 3D object detection, we investigate the effectiveness of our framework for the problem of 3D object counting.
  arXiv  Detail & Related papers  (2022-09-13T05:26:09Z)
• Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections [14.899075941080541]
  We present an unsupervised approach for discovering articulated motions in a part-segmented 3D shape collection. Our approach is based on a concept we call category closure: any valid articulation of an object's parts should keep the object in the same semantic category. We evaluate our approach by using it to re-discover part motions from the PartNet-Mobility dataset.
  arXiv  Detail & Related papers  (2022-06-17T00:50:36Z)
• The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose [108.21037046507483]
  IKEA ASM is a three million frame, multi-view, furniture assembly video dataset that includes depth, atomic actions, object segmentation, and human pose. We benchmark prominent methods for video action recognition, object segmentation and human pose estimation tasks on this challenging dataset. The dataset enables the development of holistic methods, which integrate multi-modal and multi-view data to better perform on these tasks.
  arXiv  Detail & Related papers  (2020-07-01T11:34:46Z)

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.