MooMIns -- Monocular 3D Reconstruction and Object Pose Estimation from Multiple Instances
- URL: http://arxiv.org/abs/2606.14389v1
- Date: Fri, 12 Jun 2026 12:24:50 GMT
- Title: MooMIns -- Monocular 3D Reconstruction and Object Pose Estimation from Multiple Instances
- Abstract summary: 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.
- Score: 3.8233569758620054
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Simultaneous 3D reconstruction and 6D object pose estimation from a single monocular image is an inherently ill-posed problem. In industrial settings, however, multiple instances of an object are often randomly arranged in bins, implicitly providing several views of the same object within a single image. 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. We present MooMIns, a new Gaussian-splatting-based approach that inverts the original Gaussian splatting formulation: instead of rendering a single scene from multiple cameras, we render multiple object instances from a single camera. Our method is initialized with SAM3 instance segmentation masks and a modified Structure from Motion (SfM) pipeline. In contrast to learned monocular depth estimation, we perform true geometry-based reconstruction from image evidence, avoiding hallucinations caused by training data priors. We evaluate MooMIns on synthetic and real bin-picking scenarios, and demonstrate accurate reconstruction of previously unseen objects as well as reliable pose estimation of individual instance
Related papers
- OCH3R: Object-Centric Holistic 3D Reconstruction [36.45885767128656]
We introduce OCH3R, a unified framework for Object-Centric Holistic 3D Reconstruction from a single RGB image.<n>OCH3R performs one forward pass to simultaneously predict all object instances with their 6D poses and detailed 3D reconstructions.<n>On standard indoor benchmarks, OCH3R achieves state-of-the-art performance across monocular depth estimation, open-vocabulary semantic segmentation, and RGB-only category-level 6D pose estimation.
arXiv Detail & Related papers (2026-05-13T05:17:51Z) - One2Any: One-Reference 6D Pose Estimation for Any Object [98.50085481362808]
6D object pose estimation remains challenging for many applications due to dependencies on complete 3D models, multi-view images, or training limited to specific object categories.<n>We propose a novel method One2Any that estimates the relative 6-degrees of freedom (DOF) object pose using only a single reference-single query RGB-D image.<n> Experiments on multiple benchmark datasets demonstrate that our model generalizes well to novel objects, achieving state-of-the-art accuracy and even rivaling methods that require multi-view or CAD inputs, at a fraction of compute.
arXiv Detail & Related papers (2025-05-07T03:54:59Z) - Structure from Duplicates: Neural Inverse Graphics from a Pile of
Objects [47.14442155536931]
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.
arXiv Detail & Related papers (2024-01-10T16:07:40Z) - 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) - Single-view 3D Mesh Reconstruction for Seen and Unseen Categories [69.29406107513621]
Single-view 3D Mesh Reconstruction is a fundamental computer vision task that aims at recovering 3D shapes from single-view RGB images.
This paper tackles Single-view 3D Mesh Reconstruction, to study the model generalization on unseen categories.
We propose an end-to-end two-stage network, GenMesh, to break the category boundaries in reconstruction.
arXiv Detail & Related papers (2022-08-04T14:13:35Z) - 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) - Canonical 3D Deformer Maps: Unifying parametric and non-parametric
methods for dense weakly-supervised category reconstruction [79.98689027127855]
We propose a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects.
Our method builds in a novel way on concepts from parametric deformation models, non-parametric 3D reconstruction, and canonical embeddings.
It achieves state-of-the-art results in dense 3D reconstruction on public in-the-wild datasets of faces, cars, and birds.
arXiv Detail & Related papers (2020-08-28T15:44:05Z) - Single View Metrology in the Wild [94.7005246862618]
We present a novel approach to single view metrology that can recover the absolute scale of a scene represented by 3D heights of objects or camera height above the ground.
Our method relies on data-driven priors learned by a deep network specifically designed to imbibe weakly supervised constraints from the interplay of the unknown camera with 3D entities such as object heights.
We demonstrate state-of-the-art qualitative and quantitative results on several datasets as well as applications including virtual object insertion.
arXiv Detail & Related papers (2020-07-18T22:31:33Z) - CoReNet: Coherent 3D scene reconstruction from a single RGB image [43.74240268086773]
We build on advances in deep learning to reconstruct the shape of a single object given only one RBG image as input.
We propose three extensions: (1) ray-traced skip connections that propagate local 2D information to the output 3D volume in a physically correct manner; (2) a hybrid 3D volume representation that enables building translation equivariant models; and (3) a reconstruction loss tailored to capture overall object geometry.
We reconstruct all objects jointly in one pass, producing a coherent reconstruction, where all objects live in a single consistent 3D coordinate frame relative to the camera and they do not intersect in 3D space.
arXiv Detail & Related papers (2020-04-27T17:53:07Z) - 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.