Search Results for author:
Found 5 papers, 1 papers with code
Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation
Autonomous systems need to localize and track surrounding objects in 3D space for safe motion planning.
RoIFusion: 3D Object Detection from LiDAR and Vision
When localizing and detecting 3D objects for autonomous driving scenes, obtaining information from multiple sensor (e. g. camera, LIDAR) typically increases the robustness of 3D detectors.
Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
Unlike conventional operation that directly applies MLPs on high-dimensional features of point cloud, our model goes wider by splitting features into groups in advance, and each group with certain smaller depth is only responsible for respective MLP operation, which can reduce complexity and allows to encode more useful information.
Fast Hierarchical Neural Network for Feature Learning on Point Cloud
In order to balance model performance and complexity, we introduce a novel neural network architecture exploiting local features from a manually subsampled point set.
GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud
In this paper, we propose a novel neural network for point cloud, dubbed GAPNet, to learn local geometric representations by embedding graph attention mechanism within stacked Multi-Layer-Perceptron (MLP) layers.
