Search Results for author: Hanlin Chen

Found 16 papers, 5 papers with code

FreeSplat++: Generalizable 3D Gaussian Splatting for Efficient Indoor Scene Reconstruction

1 code implementation29 Mar 2025 Yunsong Wang, Tianxin Huang, Hanlin Chen, Gim Hee Lee

After the feed-forward reconstruction of 3DGS primitives, we investigate a depth-regularized per-scene fine-tuning process.

3DGS Indoor Scene Reconstruction +1

ChatSplat: 3D Conversational Gaussian Splatting

no code implementations1 Dec 2024 Hanlin Chen, Fangyin Wei, Gim Hee Lee

Extensive experimental results demonstrate that ChatSplat supports multi-level interactions -- object, view, and scene -- within 3D space, enhancing both understanding and engagement.

Large Language Model Scene Understanding

Generalizable Human Gaussians from Single-View Image

1 code implementation10 Jun 2024 Jinnan Chen, Chen Li, Jianfeng Zhang, Lingting Zhu, Buzhen Huang, Hanlin Chen, Gim Hee Lee

To mitigate the potential generation of unrealistic human poses and shapes, we incorporate human priors from the SMPL-X model as a dual branch, propagating image features from the SMPL-X volume to the image Gaussians using sparse convolution and attention mechanisms.

Novel View Synthesis SSIM +1

VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction

no code implementations9 Jun 2024 Hanlin Chen, Fangyin Wei, Chen Li, Tianxin Huang, Yunsong Wang, Gim Hee Lee

Although 3D Gaussian Splatting has been widely studied because of its realistic and efficient novel-view synthesis, it is still challenging to extract a high-quality surface from the point-based representation.

Novel View Synthesis Surface Reconstruction

FreeSplat: Generalizable 3D Gaussian Splatting Towards Free-View Synthesis of Indoor Scenes

1 code implementation28 May 2024 Yunsong Wang, Tianxin Huang, Hanlin Chen, Gim Hee Lee

However, existing generalizable 3D Gaussian Splatting methods are largely confined to narrow-range interpolation between stereo images due to their heavy backbones, thus lacking the ability to accurately localize 3D Gaussian and support free-view synthesis across wide view range.

Novel View Synthesis Triplet

NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance

no code implementations1 Dec 2023 Hanlin Chen, Chen Li, Gim Hee Lee

In this work, we propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces.

3D Reconstruction Multi-View 3D Reconstruction +1

Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories

no code implementations22 Jan 2023 Hanlin Chen, Renyuan Luo, Yiheng Feng

Navigating CAVs in such areas heavily relies on how the vehicle defines drivable areas based on perception information.

Simultaneous Localization and Mapping

The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection

no code implementations CVPR 2023 Simin Chen, Hanlin Chen, Mirazul Haque, Cong Liu, Wei Yang

Recent advancements in deploying deep neural networks (DNNs) on resource-constrained devices have generated interest in input-adaptive dynamic neural networks (DyNNs).

Adversarial Attack Dynamic neural networks

NAS-Bench-Zero: A Large Scale Dataset for Understanding Zero-Shot Neural Architecture Search

no code implementations29 Sep 2021 Hanlin Chen, Ming Lin, Xiuyu Sun, Hao Li

Based on these new discoveries, we propose i) a novel hybrid zero-shot proxy which outperforms existing ones by a large margin and is transferable among popular search spaces; ii) a new index for better measuring the true performance of ZS-NAS proxies in constrained NAS.

Benchmarking Neural Architecture Search

Binarized Neural Architecture Search for Efficient Object Recognition

no code implementations8 Sep 2020 Hanlin Chen, Li'an Zhuo, Baochang Zhang, Xiawu Zheng, Jianzhuang Liu, Rongrong Ji, David Doermann, Guodong Guo

In this paper, binarized neural architecture search (BNAS), with a search space of binarized convolutions, is introduced to produce extremely compressed models to reduce huge computational cost on embedded devices for edge computing.

Edge-computing Face Recognition +3

Anti-Bandit Neural Architecture Search for Model Defense

no code implementations ECCV 2020 Hanlin Chen, Baochang Zhang, Song Xue, Xuan Gong, Hong Liu, Rongrong Ji, David Doermann

Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks.

Denoising model +1

Cogradient Descent for Bilinear Optimization

no code implementations CVPR 2020 Li'an Zhuo, Baochang Zhang, Linlin Yang, Hanlin Chen, Qixiang Ye, David Doermann, Guodong Guo, Rongrong Ji

Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure.

Image Reconstruction Network Pruning

CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks

no code implementations30 Apr 2020 Li'an Zhuo, Baochang Zhang, Hanlin Chen, Linlin Yang, Chen Chen, Yanjun Zhu, David Doermann

To this end, a Child-Parent (CP) model is introduced to a differentiable NAS to search the binarized architecture (Child) under the supervision of a full-precision model (Parent).

Neural Architecture Search

Binarized Neural Architecture Search

no code implementations25 Nov 2019 Hanlin Chen, Li'an Zhuo, Baochang Zhang, Xiawu Zheng, Jianzhuang Liu, David Doermann, Rongrong Ji

A variant, binarized neural architecture search (BNAS), with a search space of binarized convolutions, can produce extremely compressed models.

Neural Architecture Search

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