We introduce a novel convolution operator for point clouds that achieves rotation invariance. The maximum likelihood training of the model follows an analysis by synthesis scheme. NYU Depth Dataset V2 (2012) [Link] Deformable Shape Completion with Graph Convolutional Autoencoders (2018 CVPR) [Paper], Global-to-Local Generative Model for 3D Shapes (SIGGRAPH Asia 2018) [Paper][Code], ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning (TOG 2018) [Paper] [Code], GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction (2018) [Paper], Visual Object Networks: Image Generation with Disentangled 3D Representation (2018) [Paper], Learning to Infer and Execute 3D Shape Programs (2019)) [Paper], Learning View Priors for Single-view 3D Reconstruction (CVPR 2019) [Paper], Learning Embedding of 3D models with Quadric Loss (BMVC 2019) [Paper] [Code], CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition (ICCV 2019) [Paper][Code]. The learned point cloud representation can be useful for point cloud classification. To contribute to this Repo, you may add content through pull requests or open an issue to let me know. This work introduce ScanObjectNN, a new real-world point cloud object dataset based on scanned indoor scene data. (B) Based on those models, around 1,100 professional designers create around 22 million interior layouts. This paper proposes a deep 3D energy-based model to represent volumetric shapes. 10,800 panoramic views (in both RGB and depth) from 194,400 RGB-D images of 90 building-scale scenes of private rooms. All of the scenes are semantically annotated at the object level. UnrealCV: Virtual Worlds for Computer Vision (2017) [Link][Paper] The Segmentation Dataset provides a segmentation of 3D models based on the CAD modeling operation, including B-Rep format, mesh, and point cloud. Dense 3D Reconstructions from a Single Image (2017) [Paper], Compact Model Representation for 3D Reconstruction (2017) [Paper], Image2Mesh: A Learning Framework for Single Image 3D Reconstruction (2017) [Paper], Learning free-form deformations for 3D object reconstruction (2018) [Paper], Variational Autoencoders for Deforming 3D Mesh Models(2018 CVPR) [Paper], Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape from Images (2018 CVPR) [Paper], Model Composition from Interchangeable Components (2007) [Paper], Data-Driven Suggestions for Creativity Support in 3D Modeling (2010) [Paper], Photo-Inspired Model-Driven 3D Object Modeling (2011) [Paper], Probabilistic Reasoning for Assembly-Based 3D Modeling (2011) [Paper], A Probabilistic Model for Component-Based Shape Synthesis (2012) [Paper], Structure Recovery by Part Assembly (2012) [Paper], Fit and Diverse: Set Evolution for Inspiring 3D Shape Galleries (2012) [Paper], AttribIt: Content Creation with Semantic Attributes (2013) [Paper], Learning Part-based Templates from Large Collections of 3D Shapes (2013) [Paper], Topology-Varying 3D Shape Creation via Structural Blending (2014) [Paper], Estimating Image Depth using Shape Collections (2014) [Paper], Single-View Reconstruction via Joint Analysis of Image and Shape Collections (2015) [Paper], Interchangeable Components for Hands-On Assembly Based Modeling (2016) [Paper], Shape Completion from a Single RGBD Image (2016) [Paper], Learning to Generate Chairs, Tables and Cars with Convolutional Networks (2014) [Paper], Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis (2015, NIPS) [Paper], Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces (2015) [Paper], Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis (2015) [Paper] [Code], Multi-view 3D Models from Single Images with a Convolutional Network (2016) [Paper] [Code], View Synthesis by Appearance Flow (2016) [Paper] [Code], Voxlets: Structured Prediction of Unobserved Voxels From a Single Depth Image (2016) [Paper] [Code], 3D-R2N2: 3D Recurrent Reconstruction Neural Network (2016) [Paper] [Code], Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision (2016) [Paper], TL-Embedding Network: Learning a Predictable and Generative Vector Representation for Objects (2016) [Paper], 3D GAN: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016) [Paper], 3D Shape Induction from 2D Views of Multiple Objects (2016) [Paper], Unsupervised Learning of 3D Structure from Images (2016) [Paper], Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency (2017) [Paper], Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks (2017) [Paper] [Code], Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis (2017) [Paper] [Code], Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs (2017) [Paper] [Code], Hierarchical Surface Prediction for 3D Object Reconstruction (2017) [Paper], OctNetFusion: Learning Depth Fusion from Data (2017) [Paper] [Code], A Point Set Generation Network for 3D Object Reconstruction from a Single Image (2017) [Paper] [Code], Learning Representations and Generative Models for 3D Point Clouds (2017) [Paper] [Code], Shape Generation using Spatially Partitioned Point Clouds (2017) [Paper], PCPNET Learning Local Shape Properties from Raw Point Clouds (2017) [Paper], Transformation-Grounded Image Generation Network for Novel 3D View Synthesis (2017) [Paper] [Code], Tag Disentangled Generative Adversarial Networks for Object Image Re-rendering (2017) [Paper], 3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks (2017) [Paper] [Code], Interactive 3D Modeling with a Generative Adversarial Network (2017) [Paper], Weakly supervised 3D Reconstruction with Adversarial Constraint (2017) [Paper] [Code], SurfNet: Generating 3D shape surfaces using deep residual networks (2017) [Paper], Learning to Reconstruct Symmetric Shapes using Planar Parameterization of 3D Surface (2019) [Paper] [Code], GRASS: Generative Recursive Autoencoders for Shape Structures (SIGGRAPH 2017) [Paper] [Code] [code], 3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks (2017) [Paper][code], Neural 3D Mesh Renderer (2017) [Paper] [Code], Pix2vox: Sketch-Based 3D Exploration with Stacked Generative Adversarial Networks (2017) [Code], What You Sketch Is What You Get: 3D Sketching using Multi-View Deep Volumetric Prediction (2017) [Paper], MarrNet: 3D Shape Reconstruction via 2.5D Sketches (2017) [Paper], Learning a Multi-View Stereo Machine (2017 NIPS) [Paper], 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions (2017) [Paper], Scaling CNNs for High Resolution Volumetric Reconstruction from a Single Image (2017) [Paper], ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling (2017) [Paper], Learning Descriptor Networks for 3D Shape Synthesis and Analysis (2018 CVPR) [Project] [Paper] [Code]. 3D-FUTURE contains 20,000+ clean and realistic synthetic scenes in 5,000+ diverse rooms, which include 10,000+ unique high quality 3D instances of furniture with high resolution informative textures developed by professional designers. CoMA is a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We propose an efficient yet robust technique for on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes. We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds (CVPR 2019) [Link] Our annotation framework draws on crowdsourcing to segment surfaces from photos, and then annotate them with rich surface properties, including material, texture and contextual information. SceneNN (2016) [Link] SUNRGB-D 3D Object Detection Challenge [Link] Each object in our dataset is considered equivalent to a sequence of primitive placement. Each room has a number of actionable objects. These models have been used in the real-world production. HoME: a Household Multimodal Environment (2017) [Link] 10K scans in RGBD + reconstructed 3D models in .PLY format. ScanObjectNN: A New Benchmark Dataset and Classification Model on Real-World Data (ICCV 2019) [Link] You signed in with another tab or window. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning. MINOS leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites. We propose an efficient end-to-end permutation invariant convolution for point cloud deep learning. ShapeNet (2015) [Link] Three key open problems for point cloud object classification are identified, and a new point cloud classification neural network that achieves state-of-the-art performance on classifying objects with cluttered background is proposed. This work introduce a dataset for geometric deep learning consisting of over 1 million individual (and high quality) geometric models, each associated with accurate ground truth information on the decomposition into patches, explicit sharp feature annotations, and analytic differential properties. Our method is built atop an efficient super-voxel clustering method and a conditional random field with higher-order constraints from structural and object cues, enabling progressive dense semantic segmentation without any precomputation. All 3D objects are fully annotated with category labels. Point Cloud Library also has a good dataset catalogue. VOCASET, is a 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio. 100+ indoor scene meshes with per-vertex and per-pixel annotation. Together, I'm sure we can advance this field as a collaborative effort. MINOS: Multimodal Indoor Simulator (2017) [Link] RingNet: 3D Face Reconstruction from Single Images (2019) [Paper][Code]. Training set: 10,355 RGB-D scene images, Testing set: 2860 RGB-D images. OpenSurfaces is a large database of annotated surfaces created from real-world consumer photographs. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. (2017) [Paper], TextureGAN: Controlling Deep Image Synthesis with Texture Patches (2018 CVPR) [Paper], Gaussian Material Synthesis (2018 SIGGRAPH) [Paper], Non-stationary Texture Synthesis by Adversarial Expansion (2018 SIGGRAPH) [Paper], Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients (2018 CVPR) [Paper], LIME: Live Intrinsic Material Estimation (2018 CVPR) [Paper], Single-Image SVBRDF Capture with a Rendering-Aware Deep Network (2018) [Paper], PhotoShape: Photorealistic Materials for Large-Scale Shape Collections (2018) [Paper], Learning Material-Aware Local Descriptors for 3D Shapes (2018) [Paper], FrankenGAN: Guided Detail Synthesis for Building Mass Models An RGB-D video dataset containing 2.5 million views in more than 1500 scans, annotated with 3D camera poses, surface reconstructions, and instance-level semantic segmentations. SMF is applied to register and perform expression transfer on scans captured in-the-wild with an iPhone depth camera represented either as meshes or point clouds. Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling [Link]. IM-NET is trained to perform this assignment by means of a binary classifier. with Per-Pixel Ground Truth using Stochastic Grammars (2018) [Paper], Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image (ECCV 2018) [Paper], Language-Driven Synthesis of 3D Scenes from Scene Databases (SIGGRAPH Asia 2018) [Paper], Deep Generative Modeling for Scene Synthesis via Hybrid Representations (2018) [Paper], GRAINS: Generative Recursive Autoencoders for INdoor Scenes (2018) [Paper], SEETHROUGH: Finding Objects in Heavily Occluded Indoor Scene Images (2018) [Paper], Scan2CAD: Learning CAD Model Alignment in RGB-D Scans (CVPR 2019) [Paper] [Code], Scan2Mesh: From Unstructured Range Scans to 3D Meshes (CVPR 2019) [Paper], 3D-SIC: 3D Semantic Instance Completion for RGB-D Scans (arXiv 2019) [Paper], End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans (arXiv 2019) [Paper], A Survey of 3D Indoor Scene Synthesis (2020) [Paper], PlanIT: Planning and Instantiating Indoor Scenes with Relation Graph and Spatial Prior Networks (2019) [Paper] [Code], Feature-metric Registration: A Fast Semi-Supervised Approach for Robust Point Cloud Registration without Correspondences (CVPR 2020) [Paper][Code], Human-centric metrics for indoor scene assessment and synthesis (2020) [Paper], SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans (2020) [Paper], Recovering the Spatial Layout of Cluttered Rooms (2009) [Paper], Characterizing Structural Relationships in Scenes Using Graph Kernels (2011 SIGGRAPH) [Paper], Understanding Indoor Scenes Using 3D Geometric Phrases (2013) [Paper], Organizing Heterogeneous Scene Collections through Contextual Focal Points (2014 SIGGRAPH) [Paper], SceneGrok: Inferring Action Maps in 3D Environments (2014, SIGGRAPH) [Paper], PanoContext: A Whole-room 3D Context Model for Panoramic Scene Understanding (2014) [Paper], Learning Informative Edge Maps for Indoor Scene Layout Prediction (2015) [Paper], Rent3D: Floor-Plan Priors for Monocular Layout Estimation (2015) [Paper], A Coarse-to-Fine Indoor Layout Estimation (CFILE) Method (2016) [Paper], DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes (2016) [Paper], 3D Semantic Parsing of Large-Scale Indoor Spaces (2016) [Paper] [Code], Deep Multi-Modal Image Correspondence Learning (2016) [Paper], Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks (2017) [Paper] [Code] [Code] [Code] [Code], RoomNet: End-to-End Room Layout Estimation (2017) [Paper], Semantic Scene Completion from a Single Depth Image (2017) [Paper] [Code], Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene (2018 CVPR) [Paper] [Code], LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image (2018 CVPR) [Paper] [Code], PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image (2018 CVPR) [Paper] [Code], Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery (2018 CVPR) [Paper], Pano2CAD: Room Layout From A Single Panorama Image (2018 CVPR) [Paper], Automatic 3D Indoor Scene Modeling from Single Panorama (2018 CVPR) [Paper], Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding (2019 CVPR) [Paper] [Code], 3D-Aware Scene Manipulation via Inverse Graphics (NeurIPS 2018) [Paper] [Code], 3D Scene Reconstruction with Multi-layer Depth and Epipolar Transformers (ICCV 2019) [Paper], PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points (NIPS 2019) [Paper], Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense (ICCV 2019) [Paper & Code].
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