Search Results for author:
Found 16 papers, 10 papers with code
PolarStream: Streaming Object Detection and Segmentation with Polar Pillars
Recent works recognized lidars as an inherently streaming data source and showed that the end-to-end latency of lidar perception models can be reduced significantly by operating on wedge-shaped point cloud sectors rather then the full point cloud.
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking
Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments.
Ranked #1 on
Panoptic Segmentation
on Panoptic nuScenes test
The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior
First, we found that these rules were enough for these models to achieve a high classification accuracy on the dataset.
Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals
Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in driving behavior.
Ranked #4 on
Trajectory Prediction
on nuScenes
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving.
PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars
Recent works recognized lidars as an inherently streaming data source and showed that the end-to-end latency of lidar perception models can be reduced significantly by operating on wedge-shaped point cloud sectors rather then the full point cloud.
Ranked #23 on
LIDAR Semantic Segmentation
on nuScenes
The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes
A high-performing object detection system plays a crucial role in autonomous driving (AD).
CoverNet: Multimodal Behavior Prediction using Trajectory Sets
We instead frame the trajectory prediction problem as classification over a diverse set of trajectories.
PointPainting: Sequential Fusion for 3D Object Detection
Surprisingly, lidar-only methods outperform fusion methods on the main benchmark datasets, suggesting a gap in the literature.
nuScenes: A multimodal dataset for autonomous driving
Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.
Ranked #312 on
3D Object Detection
on nuScenes
(using extra training data)
PointPillars: Fast Encoders for Object Detection from Point Clouds
These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.
Compact Bilinear Pooling
Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition.
Quantification in-the-wild: data-sets and baselines
Quantification is the task of estimating the class-distribution of a data-set.
WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification
Manual classification of such a vast image collection is impractical due to the size of the data set.
Random Sampling in an Age of Automation: Minimizing Expenditures through Balanced Collection and Annotation
Methods for automated collection and annotation are changing the cost-structures of sampling surveys for a wide range of applications.
Efficient Large-Scale Structured Learning
Our method is shown to be 10-50 times faster than SVMstruct for cost-sensitive multiclass classification while being about as fast as the fastest 1-vs-all methods for multiclass classification.
