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ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition

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Abstract

3D decomposition/segmentation remains a challenge as large-scale 3D annotated data is not readily available. Existing approaches typically leverage 2D machine-generated segments, integrating them to achieve 3D consistency. In this paper, we propose ClusteringSDF, a novel approach achieving both segmentation and reconstruction in 3D via the neural implicit surface representation, specifically the Signed Distance Function (SDF), where the segmentation rendering is directly integrated with the volume rendering of neural implicit surfaces. Although based on ObjectSDF++, ClusteringSDFno longer requires ground-truth segments for supervision while maintaining the capability of reconstructing individual object surfaces, relying purely on the noisy and inconsistent labels from pre-trained models. As the core of ClusteringSDF, we introduce a highly efficientclustering mechanism for lifting 2D labels to 3D. Experimental results on the challenging scenes from ScanNet and Replica datasets show that ClusteringSDF  can achieve competitive performance compared to the state-of-the-art with significantly reduced training time.

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Acknowledgements

This study is supported under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). Chuanxia Zheng is supported by EPSRC SYN3D EP/Z001811/1.

Author information

Authors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Tianhao Wu & Tat-Jen Cham

  2. S-Lab, Singapore, Singapore

    Tianhao Wu

  3. VGG, University of Oxford, Oxford, UK

    Chuanxia Zheng

  4. Monash University, Melbourne, Australia

    Qianyi Wu

Authors
  1. Tianhao Wu

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  2. Chuanxia Zheng

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  3. Qianyi Wu

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  4. Tat-Jen Cham

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Corresponding author

Correspondence toTianhao Wu.

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Wu, T., Zheng, C., Wu, Q., Cham, TJ. (2025). ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15115. Springer, Cham. https://doi.org/10.1007/978-3-031-72998-0_15

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