Search Results for author:
Found 8 papers, 5 papers with code
CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers
Point cloud completion is an indispensable task for recovering complete point clouds due to incompleteness caused by occlusion, limited sensor resolution, etc.
Interpretation on Multi-modal Visual Fusion
In this paper, we present an analytical framework and a novel metric to shed light on the interpretation of the multimodal vision community.
SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer
Point cloud completion has become increasingly popular among generation tasks of 3D point clouds, as it is a challenging yet indispensable problem to recover the complete shape of a 3D object from its partial observation.
Ranked #7 on
Point Cloud Completion
on Completion3D
AGConv: Adaptive Graph Convolution on 3D Point Clouds
In this paper, we propose Adaptive Graph Convolution (AGConv) for wide applications of point cloud analysis.
Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds
Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations.
Boundary Distribution Estimation for Precise Object Detection
In the field of state-of-the-art object detection, the task of object localization is typically accomplished through a dedicated subnet that emphasizes bounding box regression.
Adaptive Graph Convolution for Point Cloud Analysis
Convolution on 3D point clouds that generalized from 2D grid-like domains is widely researched yet far from perfect.
Ranked #10 on
3D Point Cloud Classification
on IntrA
Geometry and Learning Co-Supported Normal Estimation for Unstructured Point Cloud
In this paper, we propose a normal estimation method for unstructured point cloud.
