4 research outputs found
UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree
Signed distance field (SDF) is a prominent implicit representation of 3D
meshes. Methods that are based on such representation achieved state-of-the-art
3D shape reconstruction quality. However, these methods struggle to reconstruct
non-convex shapes. One remedy is to incorporate a constructive solid geometry
framework (CSG) that represents a shape as a decomposition into primitives. It
allows to embody a 3D shape of high complexity and non-convexity with a simple
tree representation of Boolean operations. Nevertheless, existing approaches
are supervised and require the entire CSG parse tree that is given upfront
during the training process. On the contrary, we propose a model that extracts
a CSG parse tree without any supervision - UCSG-Net. Our model predicts
parameters of primitives and binarizes their SDF representation through
differentiable indicator function. It is achieved jointly with discovering the
structure of a Boolean operators tree. The model selects dynamically which
operator combination over primitives leads to the reconstruction of high
fidelity. We evaluate our method on 2D and 3D autoencoding tasks. We show that
the predicted parse tree representation is interpretable and can be used in CAD
software.Comment: Under review at Thirty-fourth Conference on Neural Information
Processing Systems (NeurIPS 2020), 13 pages, 7 figures; fix the reference to
the CSG-Net wor
On the Complexity of the CSG Tree Extraction Problem
In this short note, we discuss the complexity of the search space for the
problem of finding a CSG expression (or CSG tree) corresponding to an input
point-cloud and a list of fitted solid primitives.Comment: Add references for the programming language based approaches and the
construction of the intersection grap
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction
Parametric computer-aided design (CAD) is a standard paradigm used for the
design of manufactured objects. CAD designers perform modeling operations, such
as sketch and extrude, to form a construction sequence that makes up a final
design. Despite the pervasiveness of parametric CAD and growing interest from
the research community, a dataset of human designed 3D CAD construction
sequences has not been available to-date. In this paper we present the Fusion
360 Gallery reconstruction dataset and environment for learning CAD
reconstruction. We provide a dataset of 8,625 designs, comprising sequential
sketch and extrude modeling operations, together with a complementary
environment called the Fusion 360 Gym, to assist with performing CAD
reconstruction. We outline a standard CAD reconstruction task, together with
evaluation metrics, and present results from a novel method using neurally
guided search to recover a construction sequence from raw geometry
PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions
Inferring programs which generate 2D and 3D shapes is important for reverse
engineering, editing, and more. Training models to perform this task is
complicated because paired (shape, program) data is not readily available for
many domains, making exact supervised learning infeasible. However, it is
possible to get paired data by compromising the accuracy of either the assigned
program labels or the shape distribution. Wake-sleep methods use samples from a
generative model of shape programs to approximate the distribution of real
shapes. In self-training, shapes are passed through a recognition model, which
predicts programs that are treated as pseudo-labels for those shapes. Related
to these approaches, we introduce a novel self-training variant unique to
program inference, where program pseudo-labels are paired with their executed
output shapes, avoiding label mismatch at the cost of an approximate shape
distribution. We propose to group these regimes under a single conceptual
framework, where training is performed with maximum likelihood updates sourced
from either Pseudo-Labels or an Approximate Distribution (PLAD). We evaluate
these techniques on multiple 2D and 3D shape program inference domains.
Compared with policy gradient reinforcement learning, we show that PLAD
techniques infer more accurate shape programs and converge significantly
faster. Finally, we propose to combine updates from different PLAD methods
within the training of a single model, and find that this approach outperforms
any individual technique