57 citations · 196 across the 11 of their papers we have counts for
15 papers
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking
Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado +4
Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments. As LiDARs provide accurate illuminati…
The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior
Bassam Helou, Aditya Dusi, Anne Collin +7
Autonomous vehicles must balance a complex set of objectives. There is no consensus on how they should do so, nor on a model for specifying a desired driving behavior. We created a…
Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals
Nachiket Deo, Eric M. Wolff, Oscar Beijbom
Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in driving behavior. This uncertainty can be loosely decoupled int…
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
Holger Caesar, Juraj Kabzan, Kok Seang Tan +6
In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving. While there is a growing body of ML-based motion planners, the lack of es…
PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars
Qi Chen, Sourabh Vora, Oscar Beijbom
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…
The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes
Yiluan Guo, Holger Caesar, Oscar Beijbom +2
A high-performing object detection system plays a crucial role in autonomous driving (AD). The performance, typically evaluated in terms of mean Average Precision, does not take in…
CoverNet: Multimodal Behavior Prediction using Trajectory Sets
Tung Phan-Minh, Elena Corina Grigore, Freddy A. Boulton +2
We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for urban driving. Previous work has employed a variety of methods, including multimodal regre…
PointPainting: Sequential Fusion for 3D Object Detection
Sourabh Vora, Alex H. Lang, Bassam Helou +1
Camera and lidar are important sensor modalities for robotics in general and self-driving cars in particular. The sensors provide complementary information offering an opportunity…
nuScenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang +7
Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision t…
PointPillars: Fast Encoders for Object Detection from Point Clouds
Alex H. Lang, Sourabh Vora, Holger Caesar +3
Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud in…
Quantification in-the-wild: data-sets and baselines
Oscar Beijbom, Judy Hoffman, Evan Yao +4
Quantification is the task of estimating the class-distribution of a data-set. While typically considered as a parameter estimation problem with strict assumptions on the data-set…
Compact Bilinear Pooling
Yang Gao, Oscar Beijbom, Ning Zhang +1
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. Howe…
WHOI-Plankton- A Large Scale Fine Grained Visual Recognition Benchmark Dataset for Plankton Classification
Eric C. Orenstein, Oscar Beijbom, Emily E. Peacock +1
Planktonic organisms are of fundamental importance to marine ecosystems: they form the basis of the food web, provide the link between the atmosphere and the deep ocean, and influe…
Random Sampling in an Age of Automation: Minimizing Expenditures through Balanced Collection and Annotation
Oscar Beijbom
Methods for automated collection and annotation are changing the cost-structures of sampling surveys for a wide range of applications. Digital samples in the form of images or audi…
Domain Adaptations for Computer Vision Applications
Oscar Beijbom
A basic assumption of statistical learning theory is that train and test data are drawn from the same underlying distribution. Unfortunately, this assumption doesn't hold in many a…