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20162022
most citedUnderstanding the Effective Receptive Field in Deep Convolutional Neural Networks

806 citations · 1.5k across the 12 of their papers we have counts for

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8 papers · 1 filter

cs.CV2022★ 1 cited

Joint Forecasting of Panoptic Segmentations with Difference Attention

Colin Graber, Cyril Jazra, Wenjie Luo +2

Forecasting of a representation is important for safe and effective autonomy. For this, panoptic segmentations have been studied as a compelling representation in recent work. Howe…

cs.CV2021★ 4 cited

End-to-end Interpretable Neural Motion Planner

Wenyuan Zeng, Wenjie Luo, Simon Suo +4

In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interaction…

cs.CV2020★ 348 cited

Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net

Wenjie Luo, Bin Yang, Raquel Urtasun

In this paper we propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. By joint…

cs.CV2020★ 2 cited

Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs

Sean Segal, Eric Kee, Wenjie Luo +3

In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling effici…

cs.CV2019★ 7 cited

PIXOR: Real-time 3D Object Detection from Point Clouds

Bin Yang, Wenjie Luo, Raquel Urtasun

We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component…

cs.CV2017★ 806 cited

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

Wenjie Luo, Yujia Li, Raquel Urtasun +1

We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond t…

cs.CV2016★ 19 cited

TorontoCity: Seeing the World with a Million Eyes

Shenlong Wang, Min Bai, Gellert Mattyus +7

In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712.5 of land, 8439 of road and around 400,000 buildings. Ou…

cs.CV2016

Exploiting Semantic Information and Deep Matching for Optical Flow

Min Bai, Wenjie Luo, Kaustav Kundu +1

We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving. We build on the observation that the scene is typically composed of a…