PITX2 expression and Neanderthal introgression in HS3ST3A1 contribute to variation in tooth dimensions in modern humans.

Dental morphology varies greatly throughout evolution, including in the human lineage, but little is known about the biology of this variation. Here, we use multiomics analyses to examine the genetics of variation in tooth crown dimensions. In a human cohort with mixed continental ancestry, we detected genome-wide significant associations at 18 genome regions.

TSRNet: A Dual-Stream Network for Refining 3D Tooth Segmentation.

The field of 3D tooth segmentation has made considerable advances thanks to deep learning, but challenges remain with coarse segmentation boundaries and prediction errors. In this article, we introduce a novel learnable method to refine coarse results obtained from existing 3D tooth segmentation algorithms. The refinement framework features a dual-stream network called TSRNet (Tooth Segmentation Refinement Network) to rectify defective boundary and distance maps extracted from the coarse segmentation.

The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning.

Background: Many scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of the clinicians have an enormous impact on judgment.

HairStyle Editing via Parametric Controllable Strokes.

In this work, we propose a stroke-based hairstyle editing network, dubbed HairstyleNet, allowing users to conveniently change the hairstyles of an image in an interactive fashion. Different from previous works, we simplify the hairstyle editing process where users can manipulate local or entire hairstyles by adjusting the parameterized hair regions. Our HairstyleNet consists of two stages: a stroke parameterization stage and a stroke-to-hair generation stage.

OrthoAligner: Image-Based Teeth Alignment Prediction via Latent Style Manipulation.

In this article, we present OrthoAligner, a novel method to predict the visual outcome of orthodontic treatment in a portrait image. Unlike the state-of-the-art method, which relies on a 3D teeth model obtained from dental scanning, our method generates realistic alignment effects in images without requiring additional 3D information as input and thus making our system readily available to average users. The key of our approach is to employ the 3D geometric information encoded in an unsupervised generative model, i.

TeethGNN: Semantic 3D Teeth Segmentation With Graph Neural Networks.

In this paper, we present TeethGNN, a novel 3D tooth segmentation method based on graph neural networks (GNNs). Given a mesh-represented 3D dental model in non-euclidean domain, our method outputs accurate and fine-grained separation of each individual tooth robust to scanning noise, foreign matters (e.g.

Human Bas-Relief Generation From a Single Photograph.

We present a semi-automatic method for producing human bas-relief from a single photograph. Given an input photo of one or multiple persons, our method first estimates a 3D skeleton for each person in the image. SMPL models are then fitted to the 3D skeletons to generate a 3D guide model.

Sketch-R2CNN: An RNN-Rasterization-CNN Architecture for Vector Sketch Recognition.

Sketches in existing large-scale datasets like the recent QuickDraw collection are often stored in a vector format, with strokes consisting of sequentially sampled points. However, most existing sketch recognition methods rasterize vector sketches as binary images and then adopt image classification techniques. In this article, we propose a novel end-to-end single-branch network architecture RNN-Rasterization-CNN (Sketch-R2CNN for short) to fully leverage the vector format of sketches for recognition.

DeepSketchHair: Deep Sketch-Based 3D Hair Modeling.

We present DeepSketchHair, a deep learning based tool for modeling of 3D hair from 2D sketches. Given a 3D bust model as reference, our sketching system takes as input a user-drawn sketch (consisting of hair contour and a few strokes indicating the hair growing direction within a hair region), and automatically generates a 3D hair model, matching the input sketch. The key enablers of our system are three carefully designed neural networks, namely, S2ONet, which converts an input sketch to a dense 2D hair orientation field; O2VNet, which maps the 2D orientation field to a 3D vector field; and V2VNet, which updates the 3D vector field with respect to the new sketches, enabling hair editing with additional sketches in new views.

AutoSweep: Recovering 3D Editable Objects from a Single Photograph.

This paper presents a fully automatic framework for extracting editable 3D objects directly from a single photograph. Unlike previous methods which recover either depth maps, point clouds, or mesh surfaces, we aim to recover 3D objects with semantic parts and can be directly edited. We base our work on the assumption that most human-made objects are constituted by parts and these parts can be well represented by generalized primitives.

Model-Guided 3D Sketching.

We present a novel 3D model-guided interface for in-situ sketching on 3D planes. Our work is motivated by evolutionary design, where existing 3D objects form the basis for conceptual re-design or further design exploration. We contribute a novel workflow that exploits the geometry of an underlying 3D model to infer 3D planes on which 2D strokes drawn that are on and around the 3D model should be meaningfully projected.

3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks.

In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to receive undesirable results due to the complex appearance of human teeth (e.g.

Toward Support-free 3D Printing: A Skeletal Approach for Partitioning Models.

Minimizing support structures is crucial in reducing 3D printing material and time. Partition-based methods are efficient means in realizing this objective. Although some algorithms exist for support-free fabrication of solid models, no algorithm ever considers the problem of support-free fabrication for shell models (i.

Recovering Functional Mechanical Assemblies from Raw Scans.

This paper presents a method to reconstruct a functional mechanical assembly from raw scans. Given multiple input scans of a mechanical assembly, our method first extracts the functional mechanical parts using a motion-guided, patch-based hierarchical registration and labeling algorithm. The extracted functional parts are then parameterized from the segments and their internal mechanical relations are encoded by a graph.

Pairwise harmonics for shape analysis.

This paper introduces a simple yet effective shape analysis mechanism for geometry processing. Unlike traditional shape analysis techniques which compute descriptors per surface point up to certain neighborhoods, we introduce a shape analysis framework in which the descriptors are based on pairs of surface points. Such a pairwise analysis approach leads to a new class of shape descriptors that are more global, discriminative, and can effectively capture the variations in the underlying geometry.

Dot scissor: a single-click interface for mesh segmentation.

This paper presents a very easy-to-use interactive tool, which we call dot scissor, for mesh segmentation. The user's effort is reduced to placing only a single click where a cut is desired. Such a simple interface is made possible by a directional search strategy supported by a concavity-aware harmonic field and a robust voting scheme that selects the best isoline as the cut.

Mesh segmentation with concavity-aware fields.

This paper presents a simple and efficient automatic mesh segmentation algorithm that solely exploits the shape concavity information. The method locates concave creases and seams using a set of concavity-sensitive scalar fields. These fields are computed by solving a Laplacian system with a novel concavity-sensitive weighting scheme.

Bilateral normal filtering for mesh denoising.

Decoupling local geometric features from the spatial location of a mesh is crucial for feature-preserving mesh denoising. This paper focuses on first order features, i.e.

[China's environmental load and environmental depressurization analysis].

Based on material flow accounting and related indicators, an indicator, domestic environmental load, is formulated to measure the aggregate environmental pressure of a nation. Combining this indicator with the gross national product, an indicator for environmental efficiency is derived. The domestic environmental load is then decomposed into the rebound effect caused by economic growth and the depressurization effect induced by the efficiency increase.