Multi-level Partition of Unity on Differentiable Moving Particles
DescriptionWe introduce a differentiable moving particle representation based on the
multi-level partition of unity (MPU) to represent dynamic implicit geome-
tries. At the core of our representation are two groups of particles, named
feature particles and sample particles, which can move in space and produce
dynamic surfaces according to external velocity fields or optimization gradi-
ents. These two particle groups iteratively guide and correct each other by
alternating their roles as inputs and outputs. Each feature particle carries
a set of coefficients for a local quadratic patch. These particle patches are
assembled with partition-of-unity weights to derive a continuous implicit
global shape. Each sampling particle carries its position and orientation,
serving as dense surface samples for optimization tasks. Based on these mov-
ing particles, we develop a fully differentiable framework to infer and evolve
highly detailed implicit geometries, enhanced by a multi-level background
grid for particle adaptivity, across different inverse tasks. We demonstrated
the efficacy of our representation through various benchmark comparisons
with state-of-the-art neural representations, achieving lower memory con-
sumption, fewer training iterations, and orders of magnitude higher accuracy
in handling topologically complex objects and dynamic tracking tasks.
multi-level partition of unity (MPU) to represent dynamic implicit geome-
tries. At the core of our representation are two groups of particles, named
feature particles and sample particles, which can move in space and produce
dynamic surfaces according to external velocity fields or optimization gradi-
ents. These two particle groups iteratively guide and correct each other by
alternating their roles as inputs and outputs. Each feature particle carries
a set of coefficients for a local quadratic patch. These particle patches are
assembled with partition-of-unity weights to derive a continuous implicit
global shape. Each sampling particle carries its position and orientation,
serving as dense surface samples for optimization tasks. Based on these mov-
ing particles, we develop a fully differentiable framework to infer and evolve
highly detailed implicit geometries, enhanced by a multi-level background
grid for particle adaptivity, across different inverse tasks. We demonstrated
the efficacy of our representation through various benchmark comparisons
with state-of-the-art neural representations, achieving lower memory con-
sumption, fewer training iterations, and orders of magnitude higher accuracy
in handling topologically complex objects and dynamic tracking tasks.
Event Type
Technical Papers
TimeTuesday, 3 December 20249:00am - 12:00pm JST
LocationHall C, C Block, Level 4
