53 citations · 76 across the 11 of their papers we have counts for
12 papers
PADFormer: Pose-agnostic Anomaly Detection from Sparse View Images
Ruiqi Wang, Yiming Qian, Fenggen Yu +4
Pose-agnostic Anomaly Detection (PAD) remains challenging as anomalies can appear under arbitrary viewpoints, requiring methods to handle significant pose variations. Existing appr…
SweepNet: Unsupervised Learning Shape Abstraction via Neural Sweepers
Mingrui Zhao, Yizhi Wang, Fenggen Yu +2
Shape abstraction is an important task for simplifying complex geometric structures while retaining essential features. Sweep surfaces, commonly found in human-made objects, aid in…
DPA-Net: Structured 3D Abstraction from Sparse Views via Differentiable Primitive Assembly
Fenggen Yu, Yiming Qian, Xu Zhang +4
We present a differentiable rendering framework to learn structured 3D abstractions in the form of primitive assemblies from sparse RGB images capturing a 3D object. By leveraging…
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images
Ruiqi Wang, Akshay Gadi Patil, Fenggen Yu +1
We introduce the first active learning (AL) model for high-accuracy instance segmentation of moveable parts from RGB images of real indoor scenes. Specifically, our goal is to obta…
DCSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts
Fenggen Yu, Qimin Chen, Maham Tanveer +2
We present DCSG, a neural model composed of two dual and complementary network branches, with dropouts, for unsupervised learning of compact constructive solid geometry (CSG) r…
HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling
Fenggen Yu, Yiming Qian, Francisca Gil-Ureta +3
We present the first active learning tool for fine-grained 3D part labeling, a problem which challenges even the most advanced deep learning (DL) methods due to the significant str…
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
Fenggen Yu, Zhiqin Chen, Manyi Li +4
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive…
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes
Jiongchao Jin, Arezou Fatemi, Wallace Lira +5
We introduce RaidaR, a rich annotated image dataset of rainy street scenes, to support autonomous driving research. The new dataset contains the largest number of rainy images (58,…
PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation
Fenggen Yu, Kun Liu, Yan Zhang +2
Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. Existing models are trained for a fixed set of labels, which greatly l…
Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines
Fenggen Yu, Yan Zhang, Kai Xu +2
We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision. Given a co…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…
3D Shape Segmentation via Shape Fully Convolutional Networks
Pengyu Wang, Yuan Gan, Panpan Shui +4
We desgin a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented as graph structures in the SF…