theodoregalanos/laion-3d

Collect large 3d dataset and build models

laion-3d

Collect large 3d dataset and build models

LAION-AI/project-menu#23

Datasets

Can be:

• 3d alone
• 3d text
• 3d image

Examples:

• https://github.com/rom1504/minecraft-schematics-dataset

Models

Depth Estimation

• Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging
  • Paper: https://arxiv.org/abs/2105.14021
  • Code: https://github.com/compphoto/BoostingMonocularDepth
  • Follow up paper: https://arxiv.org/abs/2012.09365
  • Follow up code: https://github.com/aim-uofa/AdelaiDepth (although firs code includes it)
• Self-supervised Learning of Depth Inference for Multi-view Stereo
  • Paper: https://arxiv.org/abs/2104.02972
  • Code: https://github.com/JiayuYANG/Self-supervised-CVP-MVSNet

Generation

• Sketch2Model - View-Aware 3D Modeling from Single Free-Hand Sketches
  • Paper: https://arxiv.org/abs/2105.06663
  • Code: https://github.com/bennyguo/sketch2model
• SceneFormer - Indoor Scene Generation with Transformers
  • Paper: https://arxiv.org/abs/2012.09793
  • Code: https://github.com/cy94/sceneformer
• Image2Lego - Customized LEGO Set Generation from Images
  • Paper: https://arxiv.org/abs/2108.08477
  • Code: 😥
• Neural RGB-D Surface Reconstruction
  • Paper: https://arxiv.org/abs/2104.04532
  • Code: 😥
• SP-GAN - Sphere-Guided 3D Shape Generation and Manipulation
  • Paper: https://arxiv.org/abs/2108.04476
  • Code: https://github.com/liruihui/sp-gan
• Style-based Point Generator with Adversarial Rendering for Point Cloud Completion
  • Paper: https://arxiv.org/abs/2103.02535
  • Code: https://github.com/microsoft/SpareNet
• Learning to Stylize Novel Views
  • Paper: https://arxiv.org/abs/2105.13509
  • Code: https://github.com/hhsinping/stylescene
• RetrievalFuse - Neural 3D Scene Reconstruction with a Database
  • Paper: https://arxiv.org/abs/2104.00024
  • Code: https://github.com/nihalsid/retrieval-fuse
• Geometry-Free View Synthesis - Transformers and no 3D Priors
  • Paper: https://arxiv.org/abs/2104.07652
  • Code: https://github.com/CompVis/geometry-free-view-synthesis
• ShapeFormer - Transformer-based Shape Completion via Sparse Representation
  • Paper: https://arxiv.org/abs/2201.10326
  • Code: 😥

Representations (🙄)

SDF-based (mostly)

• Neural Parts - Learning Expressive 3D Shape Abstractions with Invertible Neural Representations
  • Paper: https://arxiv.org/abs/2103.10429
  • Code: https://github.com/paschalidoud/neural_parts
• DeepSDF - Learning Continuous Signed Distance Functions for Shape Representation
  • Paper: https://arxiv.org/abs/1901.05103
  • Code: https://github.com/Facebookresearch/deepsdf
• Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields
  • Paper: https://arxiv.org/abs/2106.01553
  • Code: https://github.com/microsoft/SplinePosEnc

Hashing

• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
  • Paper: https://arxiv.org/abs/2201.05989
  • Code: https://github.com/nvlabs/instant-ngp

Implicit

• Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image
  • Paper: https://openreview.net/forum?id=U8pbd00cCWB
  • Code: maskedURL lol
• NeuS - Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
  • Paper: https://arxiv.org/abs/2106.10689
  • Code: https://github.com/Totoro97/NeuS
• From data to functa - Your data point is a function
  • Paper: https://arxiv.org/abs/2201.12204
  • Code: 😥
  • Previous work: https://arxiv.org/abs/2102.04776
  • Code: https://github.com/EmilienDupont/neural-function-distributions
• Multiresolution Deep Implicit Functions for 3D Shape Representation
  • Paper: https://arxiv.org/abs/2109.05591
  • Code: 😥
• Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
  • Paper: https://arxiv.org/abs/2106.05187
  • Code: https://github.com/yifita/idf
• Implicit Neural Representations with Periodic Activation Functions
  • Paper: https://arxiv.org/abs/2006.09661
  • Follow up: https://arxiv.org/abs/2104.03960
  • Code: https://github.com/lucidrains/siren-pytorch
• Volume Rendering of Neural Implicit Surfaces
  • Paper: https://arxiv.org/abs/2106.12052
  • Code: https://github.com/ventusff/neurecon
• HyperCube - Implicit Field Representations of Voxelized 3D Models
  • Paper: https://arxiv.org/abs/2110.05770
  • Code: https://github.com/mproszewska/hypercube
• Convolutional Occupancy Networks
  • Paper: https://arxiv.org/abs/2003.04618
  • Code: https://github.com/autonomousvision/convolutional_occupancy_networks

Neural/Differential Rendering

• GANcraft - Unsupervised 3D Neural Rendering of Minecraft Worlds
  • Paper: https://arxiv.org/abs/2104.07659
  • Code: https://github.com/NVlabs/imaginaire
• ADOP: Approximate Differentiable One-Pixel Point Rendering
  • Paper: https://arxiv.org/abs/2110.06635
  • Code: https://github.com/darglein/ADOP
  • youtube:

NeRF

• Editing Conditional Radiance Fields
  • Paper: http://editnerf.csail.mit.edu/paper.pdf
  • Code: https://github.com/stevliu/editnerf
• GIRAFFE - Representing Scenes as Compositional Generative Neural Feature Fields
  • Paper: https://arxiv.org/abs/2011.12100
  • Code: https://github.com/autonomousvision/giraffe
• NeX - Real-time View Synthesis with Neural Basis Expansion
  • Paper: https://arxiv.org/abs/2103.05606
  • Code: https://github.com/nex-mpi/nex-code
• Putting NeRF on a Diet - Semantically Consistent Few-Shot View Synthesis
  • Paper: https://arxiv.org/abs/2104.00677
  • Code: https://github.com/ajayjain/DietNeRF
• Unconstrained Scene Generation with Locally Conditioned Radiance Fields
  • Paper: https://arxiv.org/abs/2104.00670
  • Code: https://github.com/apple/ml-gsn
• Zero-Shot Text-Guided Object Generation with Dream Fields
  • Paper: https://arxiv.org/abs/2112.01455
  • Code: https://github.com/google-research/google-research/tree/master/dreamfields

Diffusion

• Diffusion Probabilistic Models for 3D Point Cloud Generation
  • Paper: https://arxiv.org/abs/2103.01458
  • Code: https://github.com/luost26/diffusion-point-cloud
• 3D Shape Generation and Completion through Point-Voxel Diffusion
  • Paper: https://arxiv.org/abs/2104.03670
  • Code: https://github.com/alexzhou907/PVD
• A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
  • Paper: https://arxiv.org/abs/2112.03530
  • Code: https://github.com/zhaoyanglyu/point_diffusion_refinement (empty)

GANs

• MOGAN - Morphologic-structure-aware Generative Learning from a Single Image
  • Paper: https://arxiv.org/abs/2103.02997
  • Code: https://github.com/JinshuChen/MOGAN

Design / Practice / Creative - Related

• Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images
  • Paper: https://arxiv.org/abs/2108.09022
  • Code: https://github.com/mingjiayang/sgsdi
• ATISS - Autoregressive Transformers for Indoor Scene Synthesis
  • Paper: https://arxiv.org/abs/2110.03675
  • Code: 😥
• Computer-Aided Design as Language
  • Paper: https://arxiv.org/abs/2105.02769
  • Code: 😥
• Patch2CAD - Patchwise Embedding Learning for In-the-Wild Shape Retrieval from a Single Image
  • Paper: https://arxiv.org/abs/2108.09368
  • Code: 😥
• Modeling Artistic Workflows for Image Generation and Editing
  • Paper: https://arxiv.org/abs/2007.07238
  • Code: https://github.com/hytseng0509/ArtEditing

Benchmarks

contrastive

• (3d, text)
• (3d, image)

Synthetic Data Generation

https://github.com/google-research/kubric from uwu1

3d -> text

text -> 3d

3d -> image

image -> 3d