KineDiff3D: Kinematic-Aware Diffusion for Category-Level Articulated Object Shape Reconstruction and Generation
- URL: http://arxiv.org/abs/2510.17137v1
- Date: Mon, 20 Oct 2025 04:15:40 GMT
- Title: KineDiff3D: Kinematic-Aware Diffusion for Category-Level Articulated Object Shape Reconstruction and Generation
- Authors: WenBo Xu, Liu Liu, Li Zhang, Ran Zhang, Hao Wu, Dan Guo, Meng Wang,
- Abstract summary: Articulated objects, such as laptops and drawers, exhibit significant challenges for 3D reconstruction and pose estimation.<n>We propose KineDiff3D: Kinematic-Aware Diffusion for Category-Level Articulated Object Shape Reconstruction and Generation.
- Score: 28.822034731724013
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Articulated objects, such as laptops and drawers, exhibit significant challenges for 3D reconstruction and pose estimation due to their multi-part geometries and variable joint configurations, which introduce structural diversity across different states. To address these challenges, we propose KineDiff3D: Kinematic-Aware Diffusion for Category-Level Articulated Object Shape Reconstruction and Generation, a unified framework for reconstructing diverse articulated instances and pose estimation from single view input. Specifically, we first encode complete geometry (SDFs), joint angles, and part segmentation into a structured latent space via a novel Kinematic-Aware VAE (KA-VAE). In addition, we employ two conditional diffusion models: one for regressing global pose (SE(3)) and joint parameters, and another for generating the kinematic-aware latent code from partial observations. Finally, we produce an iterative optimization module that bidirectionally refines reconstruction accuracy and kinematic parameters via Chamfer-distance minimization while preserving articulation constraints. Experimental results on synthetic, semi-synthetic, and real-world datasets demonstrate the effectiveness of our approach in accurately reconstructing articulated objects and estimating their kinematic properties.
Related papers
- ART: Articulated Reconstruction Transformer [22.27508161142687]
We introduce ART, a category-agnostic, feed-forward model that reconstructs complete 3D articulated objects from only sparse, multi-state RGB images.<n>Our newly designed transformer architecture maps sparse image inputs to a set of learnable part slots, from which ART jointly decodes unified representations for individual parts.
arXiv Detail & Related papers (2025-12-16T18:35:23Z) - LARM: A Large Articulated-Object Reconstruction Model [29.66486888001511]
LARM is a unified feedforward framework that reconstructs 3D articulated objects from sparse-view images.<n>LARM generates auxiliary outputs such as depth maps and part masks to facilitate explicit 3D mesh extraction and joint estimation.<n>Our pipeline eliminates the need for dense supervision and supports high-fidelity reconstruction across diverse object categories.
arXiv Detail & Related papers (2025-11-14T18:55:27Z) - Self-Supervised Multi-Part Articulated Objects Modeling via Deformable Gaussian Splatting and Progressive Primitive Segmentation [23.18517560629462]
We introduce DeGSS, a unified framework that encodes articulated objects as deformable 3D Gaussian fields, embedding geometry, appearance, and motion in one compact representation.<n>To evaluate generalization and realism, we enlarge the synthetic PartNet-Mobility benchmark and release RS-Art, a real-to-sim dataset that pairs RGB captures with accurately reverse-engineered 3D models.
arXiv Detail & Related papers (2025-06-11T12:32:16Z) - GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity [49.31257173003408]
We present a novel method for 6-DoF object tracking and high-quality 3D reconstruction from monocular RGBD video.<n>Our approach demonstrates strong capabilities in recovering high-fidelity object meshes, setting a new standard for single-sensor 3D reconstruction in open-world environments.
arXiv Detail & Related papers (2025-05-17T08:46:29Z) - Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image [52.11275397911693]
We propose an end-to-end trainable, cross-category method for reconstructing multiple man-made articulated objects from a single RGBD image.<n>We depart from previous works that rely on learning instance-level latent space, focusing on man-made articulated objects with predefined part counts.<n>Our method successfully reconstructs variously structured multiple instances that previous works cannot handle, and outperforms prior works in shape reconstruction and kinematics estimation.
arXiv Detail & Related papers (2025-04-04T05:08:04Z) - ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting [66.29782808719301]
Building articulated objects is a key challenge in computer vision.<n>Existing methods often fail to effectively integrate information across different object states.<n>We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient representation.
arXiv Detail & Related papers (2025-02-26T10:25:32Z) - REACTO: Reconstructing Articulated Objects from a Single Video [64.89760223391573]
We propose a novel deformation model that enhances the rigidity of each part while maintaining flexible deformation of the joints.
Our method outperforms previous works in producing higher-fidelity 3D reconstructions of general articulated objects.
arXiv Detail & Related papers (2024-04-17T08:01:55Z) - Robust 3D Shape Reconstruction in Zero-Shot from a Single Image in the Wild [22.82439286651921]
We propose a unified regression model that integrates segmentation and reconstruction, specifically designed for 3D shape reconstruction.<n>We also introduce a scalable data synthesis pipeline that simulates a wide range of variations in objects, occluders, and backgrounds.<n>Our training on our synthetic data enables the proposed model to achieve state-of-the-art zero-shot results on real-world images.
arXiv Detail & Related papers (2024-03-21T16:40:10Z) - DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via
Deformable Template Field [29.42222066097076]
Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging.
We propose the DTF-Net, a novel framework for pose estimation and shape reconstruction based on implicit neural fields of object categories.
arXiv Detail & Related papers (2023-08-04T10:35:40Z) - Unsupervised Learning of 3D Object Categories from Videos in the Wild [75.09720013151247]
We focus on learning a model from multiple views of a large collection of object instances.
We propose a new neural network design, called warp-conditioned ray embedding (WCR), which significantly improves reconstruction.
Our evaluation demonstrates performance improvements over several deep monocular reconstruction baselines on existing benchmarks.
arXiv Detail & Related papers (2021-03-30T17:57:01Z) - Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection
Consistency [114.02182755620784]
We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision.
Our framework is shown to outperform the state-of-the-art depth and motion estimation methods.
arXiv Detail & Related papers (2021-02-04T14:26:42Z)
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.