Deformable Voxel Grids for Shape Comparisons

11/21/2022
by   Raphaël Groscot, et al.
0

We present Deformable Voxel Grids (DVGs) for 3D shapes comparison and processing. It consists of a voxel grid which is deformed to approximate the silhouette of a shape, via energy-minimization. By interpreting the DVG as a local coordinates system, it provides a better embedding space than a regular voxel grid, since it is adapted to the geometry of the shape. It also allows to deform the shape by moving the control points of the DVG, in a similar manner to the Free Form Deformation, but with easier interpretability of the control points positions. After proposing a computation scheme of the energies compatible with meshes and pointclouds, we demonstrate the use of DVGs in a variety of applications: correspondences via cubification, style transfer, shape retrieval and PCA deformations. The first two require no learning and can be readily run on any shapes in a matter of minutes on modest hardware. As for the last two, they require to first optimize DVGs on a collection of shapes, which amounts to a pre-processing step. Then, determining PCA coordinates is straightforward and brings a few parameters to deform a shape.

READ FULL TEXT

page 2

page 5

page 6

page 7

page 8

research
03/12/2020

SeqXY2SeqZ: Structure Learning for 3D Shapes by Sequentially Predicting 1D Occupancy Segments From 2D Coordinates

Structure learning for 3D shapes is vital for 3D computer vision. State-...
research
05/13/2011

Salient Local 3D Features for 3D Shape Retrieval

In this paper we describe a new formulation for the 3D salient local fea...
research
12/25/2021

GPU Accelerated Voxel Grid Generation for Fast MAV Exploration

Voxel grids are a minimal and efficient environment representation that ...
research
07/19/2017

Shape Generation using Spatially Partitioned Point Clouds

We propose a method to generate 3D shapes using point clouds. Given a po...
research
08/10/2020

RocNet: Recursive Octree Network for Efficient 3D Deep Representation

We introduce a deep recursive octree network for the compression of 3D v...
research
04/29/2018

Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers

In this paper, we develop novel, efficient 2D encodings for 3D geometry,...
research
01/13/2023

Reworking geometric morphometrics into a methodology of transformation grids

Today's typical application of geometric morphometrics to a quantitative...

Please sign up or login with your details

Forgot password? Click here to reset