ParticleSplat: Self-supervised Object-centric Latent Particle Splatting

ParticleSplat, a self-supervised object-centric learning method detailed in arXiv paper 2609.19463v1, decomposes scenes into latent "particles" representing semantic entities via feedforward 3D Gaussian Splatting. Building on the Deep Latent Particles (DLP) framework, the method introduces a 3D latent particle space trained with a novel view synthesis objective, jointly encoding multiple views with camera poses into a shared 3D object-centric latent space. On simulated and real-world datasets, ParticleSplat learns object masks without supervision, supports controllable 3D scene editing by modifying particles in latent space, and improves downstream performance on robotic manipulation tasks.

arXiv:2609.19463v1 Announce Type: new Abstract: We present ParticleSplat, a self-supervised object-centric learning method that decomposes scenes into a set of latent ''particles'' representing semantic entities through feedforward 3D Gaussian Splatting. Building on the Deep Latent Particles DLP framework, which represents images as a set of particles with attributes such as position, scale, and visual appearance, we address a key limitation of DLP: its inherently 2D nature, which prevents explicit 3D spatial and geometric reasoning that are critical for downstream tasks such as robotic manipulation. Leveraging the structural similarity between latent particles and 3D Gaussian primitives, we introduce a 3D latent particle space trained with a novel view synthesis objective. Our model jointly encodes multiple views with camera poses into a shared 3D object-centric latent space, then transforms particles into particle-aligned 3D Gaussians whose composition reconstructs the full scene. On simulated and real-world datasets, we show that this formulation inherently learns object masks without supervision and supports controllable 3D scene editing, such as moving objects by modifying particles in the latent space. We further establish that the learned 3D representation improves downstream performance on robotic manipulation tasks.