This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection, built on SECOND.

Last update: Dec 20, 2022

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Overview

3D-CVF

This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection, built on SECOND.

Requirements

Follow the installation steps in SECOND, or use the docker image we provide.

docker pull yckimm/second:second_v1.5

Getting Started

Training (1st stage)

sh train_bash_1st.sh

Training (2nd stage)

sh train_bash_2nd.sh

Acknowledge

Thanks to the SECOND codebase maintained by traveller59.

Owner

YecheolKim

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This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection, built on SECOND.

Related tags

Overview

3D-CVF

Requirements

Getting Started

Training (1st stage)

Training (2nd stage)

Acknowledge

Owner

YecheolKim

audioLIME: Listenable Explanations Using Source Separation

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Blender scripts for computing geodesic distance

Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Network Compression via Central Filter

Implementation of the paper "Shapley Explanation Networks"

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

[ICCV21] Self-Calibrating Neural Radiance Fields

SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers

ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis

[CVPR 2022] Official code for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration"

Official PyTorch implementation of "Synthesis of Screentone Patterns of Manga Characters"

A style-based Quantum Generative Adversarial Network

Official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo'

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PRTR: Pose Recognition with Cascade Transformers

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection, built on SECOND.

Related tags

Overview

3D-CVF

Requirements

Getting Started

Training (1st stage)

Training (2nd stage)

Acknowledge

Owner

YecheolKim

audioLIME: Listenable Explanations Using Source Separation

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Blender scripts for computing geodesic distance

Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Network Compression via Central Filter

Implementation of the paper "Shapley Explanation Networks"

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

[ICCV21] Self-Calibrating Neural Radiance Fields

SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers

ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis

[CVPR 2022] Official code for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration"

Official PyTorch implementation of "Synthesis of Screentone Patterns of Manga Characters"

A style-based Quantum Generative Adversarial Network

Official source code of paper 'IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo'

Robot Servers and Server Manager software for robo-gym

Source code related to the article submitted to the International Conference on Computational Science ICCS 2022 in London

Introduction to AI assignment 1 HCM University of Technology, term 211

PRTR: Pose Recognition with Cascade Transformers

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.