Virtual Sparse Convolution for Multimodal 3D Object Detection 1. Problems Early methods extended the features of LiDAR points with image features, such as semantic mask and 2D CNN feature...
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CancelPaper note 8 - Virtual Sparse Convolution for Multimodal 3D Object Detection
Github workflow
Github workflow Sync branch main Create new branch to work (create a name with meaning for everyone to know what we are doing) git reset –hard HEAD^^^ git checkout main git pull git ch...
Paper note 7 - PointRCNN - 3D Object Proposal Generation and Detection from Point Cloud
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud 1. Comparison with state-of-the-art methods In autonomous driving, the most commonly used 3D sensors are the LiDAR sensor...
3D Object Detection for Autonomous Driving - A Comprehensive Survey [Paper Lists]
3D Object Detection for Autonomous Driving: A Comprehensive Survey 1. Data Source for 3D Object Detection 1.1. Datasets for 3D Object Detection 2022 DAIR-V2X: A Large-Scale Dataset for Veh...
Common Model Evaluation Metrics for Machine Learning
Evaluating your machine learning algorithm is an essential part of any project. Your model may give you satisfying results when evaluated using a metric say accuracy_score but may give poor results...
3D Object Detection for Autonomous Driving - A Comprehensive Survey
3D Object Detection for Autonomous Driving: A Comprehensive Survey 1. Introduction 1.1. 3D Object Detection Problem definition: 3D object detection aims to predict bounding boxes of 3D objects...
Paper note 6 - PointPillars - Fast Encoders for Object Detection from Point Clouds
PointPillars: Fast Encoders for Object Detection from Point Clouds 1. Problem VoxelNet and PointNet is to slow. SECOND was improved but still slow. => PointPillars: a method for objec...
Paper note 5 - SECOND - Sparsely Embedded Convolutional Detection
SECOND - Sparsely Embedded Convolutional Detection In this paper, Authors present a novel approach called SECOND (Sparsely Embedded CONvolutional Detection), which addresses these challenges in 3D...
Paper note 4 - VoxelNet - End-to-End Learning for Point Cloud Based 3D Object Detection
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection 1. PointNet problem High computational and memory requiremente => Scaling up 3D feature learning networks to orders of ...
Paper note 3 - PointNets Series
1. PointNet - Deep Learning on Point Sets for 3D Classification and Segmentation Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transfo...