Scan2Cap[CVPR 2021] Scan2Cap: Context-aware Dense Captioning in RGB-D Scans
Stars: ✭ 81 (+97.56%)
So NetSO-Net: Self-Organizing Network for Point Cloud Analysis, CVPR2018
Stars: ✭ 297 (+624.39%)
LBYLNet[CVPR2021] Look before you leap: learning landmark features for one-stage visual grounding.
Stars: ✭ 46 (+12.2%)
HistoGANReference code for the paper HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms (CVPR 2021).
Stars: ✭ 158 (+285.37%)
cvpr-buzz🐝 Explore Trending Papers at CVPR
Stars: ✭ 37 (-9.76%)
BCNetDeep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]
Stars: ✭ 434 (+958.54%)
MetaBIN[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification
Stars: ✭ 58 (+41.46%)
CoMoGANCoMoGAN: continuous model-guided image-to-image translation. CVPR 2021 oral.
Stars: ✭ 139 (+239.02%)
RfDNetImplementation of CVPR'21: RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction
Stars: ✭ 150 (+265.85%)
AMP-RegularizerCode for our paper "Regularizing Neural Networks via Adversarial Model Perturbation", CVPR2021
Stars: ✭ 26 (-36.59%)
Jsis3d[CVPR'19] JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds
Stars: ✭ 144 (+251.22%)
KERNCode for Knowledge-Embedded Routing Network for Scene Graph Generation (CVPR 2019)
Stars: ✭ 99 (+141.46%)
AODAOfficial implementation of "Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis"(WACV 2022/CVPRW 2021)
Stars: ✭ 44 (+7.32%)
cfvqa[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias
Stars: ✭ 96 (+134.15%)
3d Bat3D Bounding Box Annotation Tool (3D-BAT) Point cloud and Image Labeling
Stars: ✭ 179 (+336.59%)
Cylinder3dRank 1st in the leaderboard of SemanticKITTI semantic segmentation (both single-scan and multi-scan) (Nov. 2020) (CVPR2021 Oral)
Stars: ✭ 221 (+439.02%)
Vision3dResearch platform for 3D object detection in PyTorch.
Stars: ✭ 177 (+331.71%)
DssDifferentiable Surface Splatting
Stars: ✭ 175 (+326.83%)
CupochRobotics with GPU computing
Stars: ✭ 225 (+448.78%)
DisplazA hackable lidar viewer
Stars: ✭ 177 (+331.71%)
MeshlabThe open source mesh processing system
Stars: ✭ 2,619 (+6287.8%)
PointnetvladPointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition, CVPR 2018
Stars: ✭ 224 (+446.34%)
TorchsparseA high-performance neural network library for point cloud processing.
Stars: ✭ 173 (+321.95%)
HandMeshNo description or website provided.
Stars: ✭ 258 (+529.27%)
DbnetDBNet: A Large-Scale Dataset for Driving Behavior Learning, CVPR 2018
Stars: ✭ 172 (+319.51%)
Semantic3dnetPoint cloud semantic segmentation via Deep 3D Convolutional Neural Network
Stars: ✭ 170 (+314.63%)
Kitti DatasetVisualising LIDAR data from KITTI dataset.
Stars: ✭ 217 (+429.27%)
Pointnet2PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Stars: ✭ 2,197 (+5258.54%)
DeFMO[CVPR 2021] DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
Stars: ✭ 144 (+251.22%)
CgalThe public CGAL repository, see the README below
Stars: ✭ 2,825 (+6790.24%)
PangolinPython binding of 3D visualization library Pangolin
Stars: ✭ 157 (+282.93%)
PointasnlPointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling (CVPR 2020)
Stars: ✭ 159 (+287.8%)
PclpyPython bindings for the Point Cloud Library (PCL)
Stars: ✭ 212 (+417.07%)
MvstudioAn integrated SfM (Structure from Motion) and MVS (Multi-View Stereo) solution.
Stars: ✭ 154 (+275.61%)
ldgcnnLinked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features
Stars: ✭ 66 (+60.98%)
soft-intro-vae-pytorch[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"
Stars: ✭ 170 (+314.63%)
Dgcnn.pytorchA PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)
Stars: ✭ 153 (+273.17%)
SamplenetDifferentiable Point Cloud Sampling (CVPR 2020 Oral)
Stars: ✭ 212 (+417.07%)
NpbgNeural Point-Based Graphics
Stars: ✭ 152 (+270.73%)
LiblasC++ library and programs for reading and writing ASPRS LAS format with LiDAR data
Stars: ✭ 211 (+414.63%)
Extrinsic lidar camera calibrationThis is a package for extrinsic calibration between a 3D LiDAR and a camera, described in paper: Improvements to Target-Based 3D LiDAR to Camera Calibration. This package is used for Cassie Blue's 3D LiDAR semantic mapping and automation.
Stars: ✭ 149 (+263.41%)
Grid GcnGrid-GCN for Fast and Scalable Point Cloud Learning
Stars: ✭ 143 (+248.78%)
Flownet3dFlowNet3D: Learning Scene Flow in 3D Point Clouds (CVPR 2019)
Stars: ✭ 249 (+507.32%)
Deepmappingcode/webpage for the DeepMapping project
Stars: ✭ 140 (+241.46%)
Frustum ConvnetThe PyTorch Implementation of F-ConvNet for 3D Object Detection
Stars: ✭ 203 (+395.12%)
Lidar camera calibrationLight-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization)
Stars: ✭ 133 (+224.39%)