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ParticleSplat is presented, a self-supervised object-centric learning method that decomposes scenes into a set of latent particles representing semantic entities through feedforward 3D Gaussian Splatting, and it is established that the learned 3D representation improves downstream performance on robotic manipulation tasks.
Disentangling object geometry from placement frame diffusion yields surprisingly high accuracy in robotic manipulation, even surpassing SE(3)-diffusion methods.
