Single Image 3D Object Estimation with Primitive Graph Networks

• URL: http://arxiv.org/abs/2109.04153v1
• Date: Thu, 9 Sep 2021 10:28:37 GMT
• Title: Single Image 3D Object Estimation with Primitive Graph Networks
• Authors: Qian He, Desen Zhou, Bo Wan, Xuming He
• Abstract summary: Reconstructing 3D object from a single image is a fundamental problem in visual scene understanding. We propose a two-stage graph network for primitive-based 3D object estimation. We train the entire graph neural network in a stage-wise strategy and evaluate it on three benchmarks: Pix3D, ModelNet and NYU Depth V2.
• Score: 30.315124364682994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing 3D object from a single image (RGB or depth) is a fundamental problem in visual scene understanding and yet remains challenging due to its ill-posed nature and complexity in real-world scenes. To address those challenges, we adopt a primitive-based representation for 3D object, and propose a two-stage graph network for primitive-based 3D object estimation, which consists of a sequential proposal module and a graph reasoning module. Given a 2D image, our proposal module first generates a sequence of 3D primitives from input image with local feature attention. Then the graph reasoning module performs joint reasoning on a primitive graph to capture the global shape context for each primitive. Such a framework is capable of taking into account rich geometry and semantic constraints during 3D structure recovery, producing 3D objects with more coherent structure even under challenging viewing conditions. We train the entire graph neural network in a stage-wise strategy and evaluate it on three benchmarks: Pix3D, ModelNet and NYU Depth V2. Extensive experiments show that our approach outperforms the previous state of the arts with a considerable margin.

Related papers

• Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture [47.44029968307207]
  We propose a novel framework for simultaneous high-fidelity recovery of object shapes and textures from single-view images. Our approach utilizes the proposed Single-view neural implicit Shape and Radiance field (SSR) representations to leverage both explicit 3D shape supervision and volume rendering. A distinctive feature of our framework is its ability to generate fine-grained textured meshes while seamlessly integrating rendering capabilities into the single-view 3D reconstruction model.
  arXiv  Detail & Related papers  (2023-11-01T11:46:15Z)
• LIST: Learning Implicitly from Spatial Transformers for Single-View 3D Reconstruction [5.107705550575662]
  List is a novel neural architecture that leverages local and global image features to reconstruct geometric and topological structure of a 3D object from a single image. We show the superiority of our model in reconstructing 3D objects from both synthetic and real-world images against the state of the art.
  arXiv  Detail & Related papers  (2023-07-23T01:01:27Z)
• Incremental 3D Semantic Scene Graph Prediction from RGB Sequences [86.77318031029404]
  We propose a real-time framework that incrementally builds a consistent 3D semantic scene graph of a scene given an RGB image sequence. Our method consists of a novel incremental entity estimation pipeline and a scene graph prediction network. The proposed network estimates 3D semantic scene graphs with iterative message passing using multi-view and geometric features extracted from the scene entities.
  arXiv  Detail & Related papers  (2023-05-04T11:32:16Z)
• 3D Surface Reconstruction in the Wild by Deforming Shape Priors from Synthetic Data [24.97027425606138]
  Reconstructing the underlying 3D surface of an object from a single image is a challenging problem. We present a new method for joint category-specific 3D reconstruction and object pose estimation from a single image. Our approach achieves state-of-the-art reconstruction performance across several real-world datasets.
  arXiv  Detail & Related papers  (2023-02-24T20:37:27Z)
• Neural Correspondence Field for Object Pose Estimation [67.96767010122633]
  We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3D object coordinates at pixels of the input image, the proposed method predicts 3D object coordinates at 3D query points sampled in the camera frustum.
  arXiv  Detail & Related papers  (2022-07-30T01:48:23Z)
• 3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow [61.62796058294777]
  Reconstructing 3D shape from a single 2D image is a challenging task. Most of the previous methods still struggle to extract semantic attributes for 3D reconstruction task. We propose 3DAttriFlow to disentangle and extract semantic attributes through different semantic levels in the input images.
  arXiv  Detail & Related papers  (2022-03-29T02:03:31Z)
• Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images [12.693545159861857]
  We propose a novel deep reinforcement learning-based approach for 3D object reconstruction from monocular images. The proposed method outperforms the state-of-the-art in terms of visual quality, reconstruction accuracy, and computational time.
  arXiv  Detail & Related papers  (2021-09-24T09:44:22Z)
• Hybrid Approach for 3D Head Reconstruction: Using Neural Networks and Visual Geometry [3.970492757288025]
  We present a novel method for reconstructing 3D heads from a single or multiple image(s) using a hybrid approach based on deep learning and geometric techniques. We propose an encoder-decoder network based on the U-net architecture and trained on synthetic data only.
  arXiv  Detail & Related papers  (2021-04-28T11:31:35Z)
• Joint Deep Multi-Graph Matching and 3D Geometry Learning from Inhomogeneous 2D Image Collections [57.60094385551773]
  We propose a trainable framework for learning a deformable 3D geometry model from inhomogeneous image collections. We in addition obtain the underlying 3D geometry of the objects depicted in the 2D images.
  arXiv  Detail & Related papers  (2021-03-31T17:25:36Z)
• Weakly Supervised Learning of Multi-Object 3D Scene Decompositions Using Deep Shape Priors [69.02332607843569]
  PriSMONet is a novel approach for learning Multi-Object 3D scene decomposition and representations from single images. A recurrent encoder regresses a latent representation of 3D shape, pose and texture of each object from an input RGB image. We evaluate the accuracy of our model in inferring 3D scene layout, demonstrate its generative capabilities, assess its generalization to real images, and point out benefits of the learned representation.
  arXiv  Detail & Related papers  (2020-10-08T14:49:23Z)
• Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image [102.44347847154867]
  We propose a novel formulation that allows to jointly recover the geometry of a 3D object as a set of primitives. Our model recovers the higher level structural decomposition of various objects in the form of a binary tree of primitives. Our experiments on the ShapeNet and D-FAUST datasets demonstrate that considering the organization of parts indeed facilitates reasoning about 3D geometry.
  arXiv  Detail & Related papers  (2020-04-02T17:58:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.