Get many 3D object shapes from images using segmentation and signed distance with ClusteringSDF
Get many 3D object shapes from images using segmentation and signed distance with ClusteringSDF
ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition
arXiv paper abstract https://arxiv.org/abs/2403.14619
arXiv PDF paper https://arxiv.org/pdf/2403.14619.pdf
3D decomposition/segmentation still remains a challenge as large-scale 3D annotated data is not readily available.
… propose ClusteringSDF, a novel approach to achieve both segmentation and reconstruction in 3D via the neural implicit surface representation,
specifically Signal 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 the ground-truth segments for supervision while maintaining the capability of reconstructing individual object surfaces, but purely with the noisy and inconsistent labels from pre-trained this http URL the core of ClusteringSDF,
… introduce a high-efficient clustering mechanism for lifting the 2D labels to 3D and the experimental results on the challenging scenes from ScanNet and Replica datasets
show that ClusteringSDF can achieve competitive performance compared against the state-of-the-art with significantly reduced training time.
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