Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects

• URL: http://arxiv.org/abs/2401.05236v1
• Date: Wed, 10 Jan 2024 16:07:40 GMT
• Title: Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects
• Abstract summary: We introduce Structure from Duplicates, a novel inverse graphics framework. It reconstructs geometry, material, and illumination from a single image containing multiple identical objects. By leveraging multi-view cues from a single image, SfD generates more realistic and detailed 3D reconstructions.
• Score: 47.14442155536931
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our world is full of identical objects (\emphe.g., cans of coke, cars of same model). These duplicates, when seen together, provide additional and strong cues for us to effectively reason about 3D. Inspired by this observation, we introduce Structure from Duplicates (SfD), a novel inverse graphics framework that reconstructs geometry, material, and illumination from a single image containing multiple identical objects. SfD begins by identifying multiple instances of an object within an image, and then jointly estimates the 6DoF pose for all instances.An inverse graphics pipeline is subsequently employed to jointly reason about the shape, material of the object, and the environment light, while adhering to the shared geometry and material constraint across instances. Our primary contributions involve utilizing object duplicates as a robust prior for single-image inverse graphics and proposing an in-plane rotation-robust Structure from Motion (SfM) formulation for joint 6-DoF object pose estimation. By leveraging multi-view cues from a single image, SfD generates more realistic and detailed 3D reconstructions, significantly outperforming existing single image reconstruction models and multi-view reconstruction approaches with a similar or greater number of observations.

Related papers

• MooMIns -- Monocular 3D Reconstruction and Object Pose Estimation from Multiple Instances [3.8233569758620054]
  Simultaneous 3D reconstruction and 6D object pose estimation from a single monocular image is an inherently ill-posed problem.<n>In industrial settings, multiple instances of an object are often randomly arranged in bins, implicitly providing several views of the same object within a single image.<n>We show that this implicit multi-view geometry can be exploited to simultaneously reconstruct the object in 3D and estimate the 6D pose of each visible object instance.
  arXiv  Detail & Related papers  (2026-06-12T12:24:50Z)
• DIPO: Dual-State Images Controlled Articulated Object Generation Powered by Diverse Data [67.99373622902827]
  DIPO is a framework for controllable generation of articulated 3D objects from a pair of images.<n>We propose a dual-image diffusion model that captures relationships between the image pair to generate part layouts and joint parameters.<n>We propose PM-X, a large-scale dataset of complex articulated 3D objects, accompanied by rendered images, URDF annotations, and textual descriptions.
  arXiv  Detail & Related papers  (2025-05-26T18:55:14Z)
• EasyHOI: Unleashing the Power of Large Models for Reconstructing Hand-Object Interactions in the Wild [79.71523320368388]
  Our work aims to reconstruct hand-object interactions from a single-view image. We first design a novel pipeline to estimate the underlying hand pose and object shape. With the initial reconstruction, we employ a prior-guided optimization scheme.
  arXiv  Detail & Related papers  (2024-11-21T16:33:35Z)
• Part123: Part-aware 3D Reconstruction from a Single-view Image [54.589723979757515]
  Part123 is a novel framework for part-aware 3D reconstruction from a single-view image. We introduce contrastive learning into a neural rendering framework to learn a part-aware feature space. A clustering-based algorithm is also developed to automatically derive 3D part segmentation results from the reconstructed models.
  arXiv  Detail & Related papers  (2024-05-27T07:10:21Z)
• 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)
• A Divide et Impera Approach for 3D Shape Reconstruction from Multiple Views [49.03830902235915]
  Estimating the 3D shape of an object from a single or multiple images has gained popularity thanks to the recent breakthroughs powered by deep learning. This paper proposes to rely on viewpoint variant reconstructions by merging the visible information from the given views. To validate the proposed method, we perform a comprehensive evaluation on the ShapeNet reference benchmark in terms of relative pose estimation and 3D shape reconstruction.
  arXiv  Detail & Related papers  (2020-11-17T09:59:32Z)
• 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)
• Self-supervised Single-view 3D Reconstruction via Semantic Consistency [142.71430568330172]
  We learn a self-supervised, single-view 3D reconstruction model that predicts the shape, texture and camera pose of a target object. The proposed method does not necessitate 3D supervision, manually annotated keypoints, multi-view images of an object or a prior 3D template.
  arXiv  Detail & Related papers  (2020-03-13T20:29:01Z)

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.