CVPR 2021 papers and code focus on 3D Obeject Detection
-
Depth-Conditioned Dynamic Message Propagation for Monocular 3D Object Detection
-
Back-Tracing Representative Points for Voting-Based 3D Object Detection in Point Clouds
-
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
-
To the Point: Efficient 3D Object Detection in the Range Image With Graph Convolution Kernels
-
3D Object Detection With Pointformer
-
Center-Based 3D Object Detection and Tracking
-
Delving Into Localization Errors for Monocular 3D Object Detection
-
3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection
-
RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union
-
PointAugmenting: Cross-Modal Augmentation for 3D Object Detection
-
LiDAR R-CNN: An Efficient and Universal 3D Object Detector code
-
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection code
-
ST3D: Self-Training for Unsupervised Domain Adaptation on 3D Object Detection code
-
LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection
-
Categorical Depth Distribution Network for Monocular 3D Object Detection
-
MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation
-
HVPR: Hybrid Voxel-Point Representation for Single-Stage 3D Object Detection code
-
PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features
-
SRDAN: Scale-Aware and Range-Aware Domain Adaptation Network for Cross-Dataset 3D Object Detection
-
Objects Are Different: Flexible Monocular 3D Object Detection code
-
M3DSSD: Monocular 3D Single Stage Object Detector code
-
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach
-
Offboard 3D Object Detection From Point Cloud Sequences