activity
20162026
most citedDeep Continuous Fusion for Multi-Sensor 3D Object Detection

430 citations · 1.2k across the 77 of their papers we have counts for

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Showing 2021Show all

8 papers · 1 filter

cs.CV2021★ 1 cited

Deep Feedback Inverse Problem Solver

Wei-Chiu Ma, Shenlong Wang, Jiayuan Gu +3

We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn…

cs.CV2021

Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving

Min Bai, Shenlong Wang, Kelvin Wong +2

In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV). Our re…

cs.CV2021★ 351 cited

Deep Parametric Continuous Convolutional Neural Networks

Shenlong Wang, Simon Suo, Wei-Chiu Ma +2

Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This limits their applica…

cs.CV2021★ 3 cited

S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling

Ze Yang, Shenlong Wang, Sivabalan Manivasagam +5

Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation.…

cs.CV2021★ 6 cited

SceneGen: Learning to Generate Realistic Traffic Scenes

Shuhan Tan, Kelvin Wong, Shenlong Wang +3

We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics…

cs.CV2021

Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild

Shivam Duggal, Zihao Wang, Wei-Chiu Ma +4

Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and grap…