Search Results for author: Qiangui Huang

Found 8 papers, 3 papers with code

Scalable Primitives for Generalized Sensor Fusion in Autonomous Vehicles

no code implementations1 Dec 2021 Sammy Sidhu, Linda Wang, Tayyab Naseer, Ashish Malhotra, Jay Chia, Aayush Ahuja, Ella Rasmussen, Qiangui Huang, Ray Gao

This paves the way for the industry to jointly design hardware and software architectures as well as large fleets with heterogeneous configurations.

3D Object Detection Autonomous Driving +1

SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies

no code implementations28 Sep 2021 Matt Vitelli, Yan Chang, Yawei Ye, Maciej Wołczyk, Błażej Osiński, Moritz Niendorf, Hugo Grimmett, Qiangui Huang, Ashesh Jain, Peter Ondruska

To combat this, our approach uses a simple yet effective rule-based fallback layer that performs sanity checks on an ML planner's decisions (e. g. avoiding collision, assuring physical feasibility).

Imitation Learning

Learning to Prune Filters in Convolutional Neural Networks

no code implementations23 Jan 2018 Qiangui Huang, Kevin Zhou, Suya You, Ulrich Neumann

Specifically, we introduce a "try-and-learn" algorithm to train pruning agents that remove unnecessary CNN filters in a data-driven way.

Semantic Segmentation

SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation

1 code implementation CVPR 2018 Weiyue Wang, Ronald Yu, Qiangui Huang, Ulrich Neumann

Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results.

3D Instance Segmentation 3D Object Detection +4

Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks

1 code implementation ICCV 2017 Weiyue Wang, Qiangui Huang, Suya You, Chao Yang, Ulrich Neumann

The 3D-ED-GAN is a 3D convolutional neural network trained with a generative adversarial paradigm to fill missing 3D data in low-resolution.

Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning

no code implementations22 Nov 2016 Qiangui Huang, Weiyue Wang, Kevin Zhou, Suya You, Ulrich Neumann

A novel neural network architecture is built for scene labeling tasks where one of the variants of the new RNN unit, Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used to model multi scale contextual dependencies in images.

Scene Labeling

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