5,340 research outputs found
Learning Material-Aware Local Descriptors for 3D Shapes
Material understanding is critical for design, geometric modeling, and
analysis of functional objects. We enable material-aware 3D shape analysis by
employing a projective convolutional neural network architecture to learn
material- aware descriptors from view-based representations of 3D points for
point-wise material classification or material- aware retrieval. Unfortunately,
only a small fraction of shapes in 3D repositories are labeled with physical
mate- rials, posing a challenge for learning methods. To address this
challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material
labels. We focus on furniture models which exhibit interesting structure and
material variabil- ity. In addition, we also contribute a high-quality expert-
labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We
further apply a mesh-aware con- ditional random field, which incorporates
rotational and reflective symmetries, to smooth our local material predic-
tions across neighboring surface patches. We demonstrate the effectiveness of
our learned descriptors for automatic texturing, material-aware retrieval, and
physical simulation. The dataset and code will be publicly available.Comment: 3DV 201
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Style-driven Shape Analysis and Synthesis
In this dissertation I will investigate algorithms that analyze stylistic properties of 3D shapes and automatically synthesize shapes given style specifications. I will start by introducing a structure-transcending method for style similarity evaluation between 3D shapes. Inspired by observations about style similarity in art history literature, we propose an algorithmically computed style similarity measure which identifies style related elements on the analyzed models and collates element-level geometric similarity measurements into an object-level style measure consistent with human perception. To achieve this consistency we employ crowdsourcing to learn the relative perceptual importance of a range of elementary shape distances and other parameters used in our measurement from participant answers to cross-structure style similarity queries. I will then describe an algorithm that utilizes this learned style similarity measure to synthesize 3D models of man-made shapes. The algorithm combines user-specified style, described via an exemplar shape, and functionality, encoded by a functionally different target shape. We transfer the exemplar style to the target via a sequence of compatible element-level operations where the compatibility is a learned metric that estimates the impact of each operation on the edited shape. We use this metric to cast style transfer as a tabu search, which incrementally updates the target shape using compatible operations, progressively increasing its style similarity to the exemplar while strictly maintaining its functionality at each step. Finally I will propose a method for reconstructing 3D shapes following style aspects of given 2D drawings. Our method takes line drawings as input and converts them into surface depth and normal maps from several output viewpoints via a deep convolutional neural network with multi-view encoder-decoder architecture. The multi-view maps are then consolidated into a dense coherent 3D point cloud by solving an optimization problem that fuses depth and normal information across all output viewpoints. The output point cloud is then converted into a polygon mesh representation, which is further fine-tuned to match the input sketch more precisely
ICAR: Image-based Complementary Auto Reasoning
Scene-aware Complementary Item Retrieval (CIR) is a challenging task which
requires to generate a set of compatible items across domains. Due to the
subjectivity, it is difficult to set up a rigorous standard for both data
collection and learning objectives. To address this challenging task, we
propose a visual compatibility concept, composed of similarity (resembling in
color, geometry, texture, and etc.) and complementarity (different items like
table vs chair completing a group). Based on this notion, we propose a
compatibility learning framework, a category-aware Flexible Bidirectional
Transformer (FBT), for visual "scene-based set compatibility reasoning" with
the cross-domain visual similarity input and auto-regressive complementary item
generation. We introduce a "Flexible Bidirectional Transformer (FBT)"
consisting of an encoder with flexible masking, a category prediction arm, and
an auto-regressive visual embedding prediction arm. And the inputs for FBT are
cross-domain visual similarity invariant embeddings, making this framework
quite generalizable. Furthermore, our proposed FBT model learns the
inter-object compatibility from a large set of scene images in a
self-supervised way. Compared with the SOTA methods, this approach achieves up
to 5.3% and 9.6% in FITB score and 22.3% and 31.8% SFID improvement on fashion
and furniture, respectively
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