Naiyan Wang

papers

2,402

total citations

papers (16)

Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training

ECCV 2020arXiv

560

citations

QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection

CVPR 2022arXiv

357

citations

Learnable Graph Matching: Incorporating Graph Partitioning With Deep Feature Learning for Multiple Object Tracking

CVPR 2021arXiv

128

citations

Anchor3DLane: Learning To Regress 3D Anchors for Monocular 3D Lane Detection

CVPR 2023arXiv

citations

GIFS: Neural Implicit Function for General Shape Representation

CVPR 2022arXiv

citations

FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models

ICCV 2023arXiv

citations

Direct Differentiable Augmentation Search

ICCV 2021arXiv

citations

Object as Query: Lifting Any 2D Object Detector to 3D Detection

ICCV 2023arXiv

citations

Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection

ICCV 2023arXiv

citations

Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion

NEURIPS 2023arXiv

citations

3D Video Object Detection With Learnable Object-Centric Global Optimization

CVPR 2023arXiv

citations

Object Concepts Emerge from Motion

NEURIPS 2025arXiv

citations

Naiyan Wang

papers (16)

Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training

QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection

Embracing Single Stride 3D Object Detector With Sparse Transformer

RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection

LiDAR R-CNN: An Efficient and Universal 3D Object Detector

Fully Sparse 3D Object Detection

Learnable Graph Matching: Incorporating Graph Partitioning With Deep Feature Learning for Multiple Object Tracking

Anchor3DLane: Learning To Regress 3D Anchors for Monocular 3D Lane Detection

GIFS: Neural Implicit Function for General Shape Representation

FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models

Direct Differentiable Augmentation Search

Object as Query: Lifting Any 2D Object Detector to 3D Detection

Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection

Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion

3D Video Object Detection With Learnable Object-Centric Global Optimization

Object Concepts Emerge from Motion

papers (16)

Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training

QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection

Embracing Single Stride 3D Object Detector With Sparse Transformer

RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection

LiDAR R-CNN: An Efficient and Universal 3D Object Detector

Fully Sparse 3D Object Detection

Learnable Graph Matching: Incorporating Graph Partitioning With Deep Feature Learning for Multiple Object Tracking

Anchor3DLane: Learning To Regress 3D Anchors for Monocular 3D Lane Detection

GIFS: Neural Implicit Function for General Shape Representation

FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models

Direct Differentiable Augmentation Search

Object as Query: Lifting Any 2D Object Detector to 3D Detection

Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection

Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion

3D Video Object Detection With Learnable Object-Centric Global Optimization

Object Concepts Emerge from Motion