ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition (doi:10.21979/N9/RJUHMC)

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

doi:10.21979/N9/RJUHMC

DR-NTU (Data)

2024-09-25

1

Wu, Tianhao; Zheng, Chuanxia; Wu, Qianyi; Cham, Tat-Jen, 2024, "ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition", https://doi.org/10.21979/N9/RJUHMC, DR-NTU (Data), V1

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

doi:10.21979/N9/RJUHMC

Wu, Tianhao (Nanyang Technological University)

Zheng, Chuanxia (University of Oxford)

Wu, Qianyi (Monash University)

Cham, Tat-Jen (Nanyang Technological University)

NA

Industry Collaboration Projects (IAF-ICP) Funding Initiative

SYN3D EP/Z001811/1

DR-NTU (Data)

Wu, Tianhao

Wu, Tianhao

2024-09-19

https://doi.org/10.21979/N9/RJUHMC

Computer and Information Science, Computer and Information Science, 3D segmentation, Neural implicit surface representation, Clustering

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++, ClusteringSDF no 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 efficient clustering 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.

Code and data

S-Lab License 1.0 <br/> Copyright 2024 S-Lab <br/><br/> Redistribution and use for non-commercial purpose in source and binary forms, with or without modification, are permitted provided that the following conditions are met: <br/> 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. <br/> 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. <br/> 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. <br/><br/> THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. <br/><br/> In the event that redistribution and/or use for commercial purpose in source or binary forms, with or without modification is required, please contact the contributor(s) of the work.

GitHub page: <a href="https://github.com/Sm0kyWu/ClusteringSDF/tree/main">Link</a>

Project page: <a href="https://sm0kywu.github.io/ClusteringSDF/">Link</a>

10.48550/arXiv.2403.14619

Wu, T., Zheng, C., Cham, T. J., & Wu, Q. (2024). ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition. arXiv preprint arXiv:2403.14619.

10356/180249

Wu, T., Zheng, C., Cham, T. J., & Wu, Q. (2024). ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition. arXiv preprint arXiv:2403.14619.

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ClusteringSDF Self-Organized Neural Implicit Surfaces for 3D Decomposition.pdf

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