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Found 10 papers, 2 papers with code
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection
We propose a late-to-early recurrent feature fusion scheme for 3D object detection using temporal LiDAR point clouds.
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting
To study the effect of these modular approaches, design new paradigms that mitigate these limitations, and accelerate the development of end-to-end motion forecasting models, we augment the Waymo Open Motion Dataset (WOMD) with large-scale, high-quality, diverse LiDAR data for the motion forecasting task.
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds
To alleviate the cost of hyperparameter tuning and iterative pseudo labeling, we develop a population-based data augmentation framework for 3D detection, named AutoPseudoAugment.
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
Data augmentations are important in training high-performance 3D object detectors for point clouds.
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds
3D object detection in point clouds is a core component for modern robotics and autonomous driving systems.
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds
Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving.
Multi-Class 3D Object Detection with Single-Class Supervision
While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost.
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems.
Ranked #101 on
Image Classification
on ImageNet
(using extra training data)
Improving 3D Object Detection through Progressive Population Based Augmentation
Data augmentation has been widely adopted for object detection in 3D point clouds.
Deep Learning-Enhanced Variational Monte Carlo Method for Quantum Many-Body Physics
Artificial neural networks have been successfully incorporated into variational Monte Carlo method (VMC) to study quantum many-body systems.
Strongly Correlated Electrons Disordered Systems and Neural Networks
