HoME: a Household Multimodal Environment (2017) [Link] Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning. , Stanford CS231A: Computer Vision-From 3D Reconstruction to Recognition (Winter 2018), UCSD CSE291-I00: Machine Learning for 3D Data (Winter 2018), Stanford CS468: Machine Learning for 3D Data (Spring 2017), Princeton COS 526: Advanced Computer Graphics (Fall 2010), Princeton CS597: Geometric Modeling and Analysis (Fall 2003). Real-time Progressive 3D Semantic Segmentation for Indoor Scenes (WACV 2019) [Link] 10K scans in RGBD + reconstructed 3D models in .PLY format. We propose an efficient yet robust technique for on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes. Each object in our dataset is considered equivalent to a sequence of primitive placement. MINOS is a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. 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. I'll use the following icons to differentiate 3D representations: To find related papers and their relationships, check out Connected Papers, which provides a neat way to visualize the academic field in a graph representation. VOCASET, is a 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio. Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling [Link]. The learned point cloud representation can be useful for point cloud classification. 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. PASCAL3D+ (2014) [Link] Tasks: region proposal generation, 2D object detection, joint 2D detection and 3D object pose estimation, and image-based 3D shape retrieval. There are a total 120 scenes in version 1.0 of the THOR environment covering four different room categories: kitchens, living rooms, bedrooms, and bathrooms. An energy-based 3D shape descriptor network is a deep energy-based model for volumetric shape patterns. MINOS leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites. We propose pointwise convolution that performs on-the-fly voxelization for learning local features of a point cloud. The Fusion 360 Gallery Dataset contains rich 2D and 3D geometry data derived from parametric CAD models. House3D is a virtual 3D environment which consists of 45K indoor scenes equipped with a diverse set of scene types, layouts and objects sourced from the SUNCG dataset. It implies that we can quickly obtain a sequential generation process that is a human assembling mechanism. The dataset has 12 subjects and 480 sequences of about 3-4 seconds each with sentences chosen from an array of standard protocols that maximize phonetic diversity. The model can be trained by MCMC-based maximum likelihood learning, or a short-run MCMC toward the energy-based model as a flow-like generator for point cloud reconstruction and interpolation. ScanNet (2017) [Link] Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer [Paper][Site][Code], Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning [Paper][Code], NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis [Project][Paper][Code], GAMesh: Guided and Augmented Meshing for Deep Point Networks (3DV 2020) [Project] [Paper] [Code], Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis (2020 TPAMI) [Paper]. InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset [Link] Parsing of Large-scale 3D Point Clouds (2017) [Paper], Semantic Segmentation of Indoor Point Clouds using Convolutional Neural Networks (2017) [Paper], SEGCloud: Semantic Segmentation of 3D Point Clouds (2017) [Paper], Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 (2017) [Paper]. These models have been used in the real-world production. VOCA is a simple and generic speech-driven facial animation framework that works across a range of identities. 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. Together, I'm sure we can advance this field as a collaborative effort. 100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D shapes. The model combines a linear identity shape space (trained from 3800 scans of human heads) with an articulated neck, jaw, and eyeballs, pose-dependent corrective blendshapes, and additional global expression blendshapes. Combinatorial 3D Shape Dataset (2020) [Link][Paper] To sum up, the characteristics of our combinatorial 3D shape dataset are (i) combinatorial, (ii) sequential, (iii) decomposable, and (iv) manipulable. 12 categories, on average 3k+ objects per category, for 3D object detection and pose estimation. AI2-THOR: Photorealistic Interactive Environments for AI Agents [Link] Using such datasets can further narrow down the discrepency between virtual environment and real world. Pointwise Convolutional Neural Networks (CVPR 2018) [Link] Texture Synthesis Using Convolutional Neural Networks (2015) [Paper], Two-Shot SVBRDF Capture for Stationary Materials (SIGGRAPH 2015) [Paper], Reflectance Modeling by Neural Texture Synthesis (2016) [Paper], Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks (2017) [Paper], High-Resolution Multi-Scale Neural Texture Synthesis (2017) [Paper], Reflectance and Natural Illumination from Single Material Specular Objects Using Deep Learning (2017) [Paper], Joint Material and Illumination Estimation from Photo Sets in the Wild (2017) [Paper], JWhat Is Around The Camera? MINOS: Multimodal Indoor Simulator (2017) [Link] 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]. using Style-Synchonized GANs (2018 SIGGRAPH Asia) [Paper], Style-Content Separation by Anisotropic Part Scales (2010) [Paper], Design Preserving Garment Transfer (2012) [Paper], Analogy-Driven 3D Style Transfer (2014) [Paper], Elements of Style: Learning Perceptual Shape Style Similarity (2015) [Paper] [Code], Functionality Preserving Shape Style Transfer (2016) [Paper] [Code], Unsupervised Texture Transfer from Images to Model Collections (2016) [Paper], Learning Detail Transfer based on Geometric Features (2017) [Paper], Co-Locating Style-Defining Elements on 3D Shapes (2017) [Paper], Neural 3D Mesh Renderer (2017) [Paper] [Code], Appearance Modeling via Proxy-to-Image Alignment (2018) [Paper], Automatic Unpaired Shape Deformation Transfer (SIGGRAPH Asia 2018) [Paper], 3DSNet: Unsupervised Shape-to-Shape 3D Style Transfer (2020) [Paper] [Code], Make It Home: Automatic Optimization of Furniture Arrangement (2011, SIGGRAPH) [Paper], Interactive Furniture Layout Using Interior Design Guidelines (2011) [Paper], Synthesizing Open Worlds with Constraints using Locally Annealed Reversible Jump MCMC (2012) [Paper], Example-based Synthesis of 3D Object Arrangements (2012 SIGGRAPH Asia) [Paper], Sketch2Scene: Sketch-based Co-retrieval and Co-placement of 3D Models (2013) [Paper], Action-Driven 3D Indoor Scene Evolution (2016) [Paper], The Clutterpalette: An Interactive Tool for Detailing Indoor Scenes (2015) [Paper], Image2Scene: Transforming Style of 3D Room (2015) [Paper], Relationship Templates for Creating Scene Variations (2016) [Paper], Predicting Complete 3D Models of Indoor Scenes (2017) [Paper], Complete 3D Scene Parsing from Single RGBD Image (2017) [Paper], Raster-to-Vector: Revisiting Floorplan Transformation (2017, ICCV) [Paper] [Code], Fully Convolutional Refined Auto-Encoding Generative Adversarial Networks for 3D Multi Object Scenes (2017) [Blog], Adaptive Synthesis of Indoor Scenes via Activity-Associated Object Relation Graphs (2017 SIGGRAPH Asia) [Paper], Automated Interior Design Using a Genetic Algorithm (2017) [Paper], SceneSuggest: Context-driven 3D Scene Design (2017) [Paper], A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition (2017) [Paper], Human-centric Indoor Scene Synthesis Using Stochastic Grammar (2018, CVPR)[Paper] [Supplementary] [Code], FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans (2018) [Paper] [Code], Deep Convolutional Priors for Indoor Scene Synthesis (2018) [Paper], Fast and Flexible Indoor scene synthesis via Deep Convolutional Generative Models (2018) [Paper] [Code], Configurable 3D Scene Synthesis and 2D Image Rendering 19 object categories for predicting a 3D bounding box in real world dimension Join the community with this link. ObjectNet3D: A Large Scale Database for 3D Object Recognition (2016) [Link] Open Surfaces: A Richly Annotated Catalog of Surface Appearance (SIGGRAPH 2013) [Link] System Overview: an end-to-end pipeline to render an RGB-D-inertial benchmark for large scale interior scene understanding and mapping. NYU Depth Dataset V2 (2012) [Link] Compared to other 3D object datasets, our proposed dataset contains an assembling sequence of unit primitives. SUNCG: A Large 3D Model Repository for Indoor Scenes (2017) [Link] Dataset for IKEA 3D models and aligned images (2013) [Link] Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation [Paper] [Code]. VOCA: Voice Operated Character Animation (2019) [Paper][Video][Code] ModelNet10: 4899 models from 10 categories By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Furthermore, we can sample valid random sequences from a given combinatorial shape after validating the sampled sequences. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. Shape My Face (SMF) is a point cloud to mesh auto-encoder for the registration of raw human face scans, and the generation of synthetic human faces. We propose an efficient end-to-end permutation invariant convolution for point cloud deep learning. A Morphable Model For The Synthesis Of 3D Faces (1999) [Paper][Code]. Large-Scale Point Cloud Classification Benchmark, which provides a large labelled 3D point cloud data set of natural scenes with over 4 billion points in total, and also covers a range of diverse urban scenes. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. The codebase demonstrates how to synthesize realistic character animations given an arbitrary speech signal and a static character mesh. To contribute to this Repo, you may add content through pull requests or open an issue to let me know. (B) Based on those models, around 1,100 professional designers create around 22 million interior layouts. The maximum likelihood training of the model follows an analysis by synthesis scheme and can be interpreted as a mode seeking and mode shifting process. We introduce a novel convolution operator for point clouds that achieves rotation invariance. Rotation Invariant Convolutions for 3D Point Clouds Deep Learning (3DV 2019) [Link] Generative PointNet is an energy-based model of unordered point clouds, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network. ScanObjectNN: A New Benchmark Dataset and Classification Model on Real-World Data (ICCV 2019) [Link] It has a total number of 58,696 mechanical components with 68 classes. 1449 densely labeled pairs of aligned RGB and depth images from Kinect video sequences for a variety of indoor scenes. 10,000 models from featured things on thingiverse.com, suitable for testing 3D printing techniques such as structural analysis , shape optimization, or solid geometry operations. We jointly address the problems of semantic and instance segmentation of 3D point clouds with a multi-task pointwise network that simultaneously performs two tasks: predicting the semantic classes of 3D points and embedding the points into high-dimensional vectors so that points of the same object instance are represented by similar embeddings. Princeton Shape Benchmark (2003) [Link] Scene Understanding (Another more detailed repository), To see a survey of RGBD datasets, check out Michael Firman's, Scene Understanding (Another more detailed. VOCASET: Speech-4D Head Scan Dataset (2019( [Link][Paper] MINOS supports SUNCG and Matterport3D scenes. Each room has a number of actionable objects. A dataset that is large in scale, well organized and richly annotated.
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machine learning 3d modeling