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Found 12 papers, 3 papers with code
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.
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet
To help contextualize progress on ImageNet and provide a more meaningful evaluation for today's state-of-the-art models, we manually review and categorize every remaining mistake that a few top models make in order to provide insight into the long-tail of errors on one of the most benchmarked datasets in computer vision.
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels
3D object detection is vital for many robotics applications.
Scene Transformer: A unified architecture for predicting multiple agent trajectories
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
Pseudo-labeling for Scalable 3D Object Detection
To safely deploy autonomous vehicles, onboard perception systems must work reliably at high accuracy across a diverse set of environments and geographies.
Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
Streaming Object Detection for 3-D Point Clouds
This built-in data capture latency is artificial, and based on treating the point cloud as a camera image in order to leverage camera-inspired architectures.
Improving 3D Object Detection through Progressive Population Based Augmentation
Data augmentation has been widely adopted for object detection in 3D point clouds.
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.
StarNet: Targeted Computation for Object Detection in Point Clouds
We show how our redesign---namely using only local information and using sampling instead of learned proposals---leads to a significantly more flexible and adaptable system: we demonstrate how we can vary the computational cost of a single trained StarNet without retraining, and how we can target proposals towards areas of interest with priors and heuristics.
