Search Results for author: Wenyu Han

Found 6 papers, 2 papers with code

Learning When to See for Long-term Traffic Data Collection on Power-constrained Devices

no code implementations25 Jan 2024 Ruixuan Zhang, Wenyu Han, Zilin Bian, Kaan Ozbay, Chen Feng

We introduce a novel learning-based framework that strategically decides observation timings for battery-powered devices and reconstructs the full data stream from sparsely sampled observations, resulting in minimal performance loss and a significantly prolonged system lifetime.

AutoEncoding Tree for City Generation and Applications

no code implementations27 Sep 2023 Wenyu Han, Congcong Wen, Lazarus Chok, Yan Liang Tan, Sheung Lung Chan, Hang Zhao, Chen Feng

Based on this dataset, we propose AETree, a tree-structured auto-encoder neural network, for city generation.

Autonomous Driving

Simultaneous Navigation and Construction Benchmarking Environments

1 code implementation31 Mar 2021 Wenyu Han, Chen Feng, Haoran Wu, Alexander Gao, Armand Jordana, Dong Liu, Lerrel Pinto, Ludovic Righetti

We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design.

Benchmarking Reinforcement Learning (RL) +2

AETree: Areal Spatial Data Generation

no code implementations1 Jan 2021 Congcong Wen, Wenyu Han, Hang Zhao, Chen Feng

Areal spatial data represent not only geographical locations but also sizes and shapes of physical objects such as buildings in a city.

Clustering

Mobile Construction Benchmark

no code implementations1 Jan 2021 Wenyu Han, Chen Feng, Haoran Wu, Alexander Gao, Armand Jordana, Dongdong Liu, Lerrel Pinto, Ludovic Righetti

We need intelligent robots to perform mobile construction, the process of moving in an environment and modifying its geometry according to a design plan.

SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings

1 code implementation CVPR 2020 Wenyu Han, Siyuan Xiang, Chenhui Liu, Ruoyu Wang, Chen Feng

Our experiments show that although convolutional networks have achieved superhuman performance in many visual learning tasks, their spatial reasoning performance on SPARE3D tasks is either lower than average human performance or even close to random guesses.

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