Search Results for author: Fenggen Yu

Found 9 papers, 1 papers with code

Coarse-to-Fine Active Segmentation of Interactable Parts in Real Scene Images

no code implementations21 Mar 2023 Ruiqi Wang, Akshay Gadi Patil, Fenggen Yu, Hao Zhang

We introduce the first active learning (AL) framework for high-accuracy instance segmentation of dynamic, interactable parts from RGB images of real indoor scenes.

Active Learning Instance Segmentation +1

D$^2$CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropouts

no code implementations27 Jan 2023 Fenggen Yu, Qimin Chen, Maham Tanveer, Ali Mahdavi Amiri, Hao Zhang

We present D$^2$CSG, a neural model composed of two dual and complementary network branches, with dropouts, for unsupervised learning of compact constructive solid geometry (CSG) representations of 3D CAD shapes.

HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling

no code implementations25 Jan 2023 Fenggen Yu, Yiming Qian, Francisca Gil-Ureta, Brian Jackson, Eric Bennett, Hao Zhang

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 structural variations among the small and intricate parts.

Active Learning

CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly

no code implementations CVPR 2022 Fenggen Yu, Zhiqin Chen, Manyi Li, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri, Hao Zhang

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 assemblies.

PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation

no code implementations CVPR 2019 Fenggen Yu, Kun Liu, Yan Zhang, Chenyang Zhu, Kai Xu

Meanwhile, to increase the segmentation accuracy at each node, we enhance the recursive contextual feature with the shape feature extracted for the corresponding part.

Ranked #12 on 3D Part Segmentation on ShapeNet-Part (Class Average IoU metric)

3D Instance Segmentation 3D Part Segmentation

Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines

no code implementations18 Apr 2018 Fenggen Yu, Yan Zhang, Kai Xu, Ali Mahdavi-Amiri, Hao Zhang

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.

3D Shape Segmentation via Shape Fully Convolutional Networks

no code implementations28 Feb 2017 Pengyu Wang, Yuan Gan, Panpan Shui, Fenggen Yu, Yan Zhang, Songle Chen, Zhengxing Sun

3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images.

Image Segmentation Semantic Segmentation

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