1 citations · 2 across the 3 of their papers we have counts for
7 papers
Multi-resolution Monocular Depth Map Fusion by Self-supervised Gradient-based Composition
Yaqiao Dai, Renjiao Yi, Chenyang Zhu +2
Monocular depth estimation is a challenging problem on which deep neural networks have demonstrated great potential. However, depth maps predicted by existing deep models usually l…
6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features
Chenyi Liu, Fei Chen, Lu Deng +5
The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well…
DisARM: Displacement Aware Relation Module for 3D Detection
Yao Duan, Chenyang Zhu, Yuqing Lan +3
We introduce Displacement Aware Relation Module (DisARM), a novel neural network module for enhancing the performance of 3D object detection in point cloud scenes. The core idea of…
ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion
Jiazhao Zhang, Chenyang Zhu, Lintao Zheng +1
Online reconstruction based on RGB-D sequences has thus far been restrained to relatively slow camera motions (<1m/s). Under very fast camera motion (e.g., 3m/s), the reconstructio…
AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri +3
We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds. Differently from the familiar single-insta…
SCORES: Shape Composition with Recursive Substructure Priors
Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri +2
We introduce SCORES, a recursive neural network for shape composition. Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of t…