14 citations · 20 across the 11 of their papers we have counts for
10 papers
RayMVSNet: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo
Junhua Xi, Yifei Shi, Yijie Wang +2
Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of ou…
Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction
Qijin She, Ruizhen Hu, Juzhan Xu +3
We approach the problem of high-DOF reaching-and-grasping via learning joint planning of grasp and motion with deep reinforcement learning. To resolve the sample efficiency issue i…
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…
3DRM:Pair-wise relation module for 3D object detection
Yuqing Lan, Yao Duan, Yifei Shi +2
Context has proven to be one of the most important factors in object layout reasoning for 3D scene understanding. Existing deep contextual models either learn holistic features for…
On Upper Bounds in Dimension Gaps of CFT's
Tristan C. Collins, Daniel Jafferis, Cumrun Vafa +2
We consider CFT's arising from branes probing singularities of internal manifolds. We focus on holographic models with internal space including arbtirary Sasaki-Einstein manifolds…
Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision
Yan Liu, Qingyong Hu, Yinjie Lei +3
Learning dense point-wise semantics from unstructured 3D point clouds with fewer labels, although a realistic problem, has been under-explored in literature. While existing weakly…