Recovering the appearance and physical parameters of elastic objects from multi-view video is essential for many applications that require simulation of the real world. Past methods for this task have provided accurate results in recovering physical properties; however, their re- liance on Neural Radiance Fields (NeRFs) for novel view synthesis means they trade off visual quality for rendering speed. To address this issue, we present a novel framework for the joint appearance reconstruction and physical parameter estimation of elastic objects relying on 3D Gaussian splatting. Our key insight is that dynamic 3D Gaussian kernels extracted from multi-view video can be used to reconstruct the object’s geometry and supervise elastic parame- ter fitting through a differentiable physics engine. Novel views and object behaviours can then be constructed by forward simulating the extracted mesh and using it to drive the Gaussian kernels. We demon- strate that our method is competitive with the state of the art on phys- ical parameter estimation while being better at reconstructing object appearance. Additionally, our method can simulate novel views and object interactions at near real-time rates that outperform past ap- proaches.

Indirect illumination is an essential part of realistic computer-generated imagery. However, accurate calculation of indirect illumination comes at high compute costs. To this end, we replace lengthy indirect illumination paths by employing an ambient light cache based on photon mapping principles. By only performing cache queries after a certain path length has been reached, we show that significantly fewer photons than traditional photon mapping techniques are sufficient and that an inaccurate data structure can be used to store them. Despite these simplifications, our method generally outperforms unidirectional path tracing while adding little time overhead.