Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ)

Last update: Jun 22, 2022

Related tags

Deep Learning Real2CAD-3DV

Overview

Real2CAD-3DV

Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ)

Group Member: Yue Pan, Yuanwen Yue, Bingxin Ke, Yujie He

Supervisor: Dr. Iro Armeni, Shengyu Huang

report | presentation | demo

Data preparation and preprocessing

TBA

How to use

Train the model, monitor it via wandb

cd ./src
# configure the path and parameters in train_scannet.sh 
bash train_scannet.sh

Evaluate the model on ScanNet or 2D3DS dataset

# configure the path and parameters in eval_xxx.sh
bash eval_scannet.sh
bash eval_2d3ds.sh

Related Projects

We thanks greatly for the following projects for the backbone and the datasets.

Owner

GitHub Repository

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Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ)

Related tags

Overview

Real2CAD-3DV

Data preparation and preprocessing

How to use

Related Projects

Owner

Official implementation of deep Gaussian process (DGP)-based multi-speaker speech synthesis with PyTorch.

PyTorch implementation of: Michieli U. and Zanuttigh P., "Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations", CVPR 2021.

Flexible-Modal Face Anti-Spoofing: A Benchmark

[ICLR 2021] Is Attention Better Than Matrix Decomposition?

Code for HLA-Face: Joint High-Low Adaptation for Low Light Face Detection (CVPR21)

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

Denoising Normalizing Flow

dyld_shared_cache processing / Single-Image loading for BinaryNinja

Pytorch codes for Feature Transfer Learning for Face Recognition with Under-Represented Data

This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes.

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Pretraining Representations For Data-Efficient Reinforcement Learning

Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

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Adapter-BERT: Parameter-Efficient Transfer Learning for NLP.

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