In this paper, we tackle the problem of unsupervised 3D object segmentation from a point cloud without RGB information. In particular, we propose a framework, ~%DIFDELCMD < {\bf %%% SPAIR3D , to model a point cloud as a spatial mixture model and jointly learn the multiple-object representation and segmentation in 3D via Variational Autoencoders (VAE). Inspired by SPAIR, we adopt an object-specification scheme that describes each object's location relative to its local voxel grid cell rather than the point cloud as a whole. To model the spatial mixture model on point clouds, we derive the ~Chamfer Likelihood , which fits naturally into the variational training pipeline. We further design a new spatially invariant graph neural network to generate a varying number of 3D points as a decoder within our VAE. ~ Experimental results demonstrate that ~%DIFDELCMD < {\bf %%% SPAIR3D is capable of detecting and segmenting variable number of objects without appearance information across diverse scenes.
In this paper, we tackle the problem of unsupervised 3D object segmentation from a point cloud without RGB information. In particular, we propose a framework, %DIFDELCMD < {\bf %%% SPAIR3D , to model a point cloud as a spatial mixture model and jointly learn the multiple-object representation and segmentation in 3D via Variational Autoencoders (VAE). Inspired by SPAIR, we adopt an object-specification scheme that describes each object's location relative to its local voxel grid cell rather than the point cloud as a whole. To model the spatial mixture model on point clouds, we derive the Chamfer Likelihood , which fits naturally into the variational training pipeline. We further design a new spatially invariant graph neural network to generate a varying number of 3D points as a decoder within our VAE. Experimental results demonstrate that %DIFDELCMD < {\bf %%% SPAIR3D is capable of detecting and segmenting variable number of objects without appearance information across diverse scenes.
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Chamfer Likelihood
Chamfer Likelihood
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