Can Single-View Mesh Reconstruction Generalize to Robot Camera Rotation?
- URL: http://arxiv.org/abs/2606.22987v1
- Date: Mon, 22 Jun 2026 08:05:45 GMT
- Title: Can Single-View Mesh Reconstruction Generalize to Robot Camera Rotation?
- Abstract summary: Single-view mesh reconstruction predicts object meshes and spatial layouts from a single observation.<n>Current single-view mesh reconstruction methods generalize poorly to robot camera rotation.<n>A two-stage SAM3D+FoundationPose pipeline is more robust than one-stage feed-forward layout prediction.
- Score: 5.6483741813289745
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Single-view mesh reconstruction predicts object meshes and spatial layouts from a single observation, making it attractive for fast robot spatial reasoning and real-to-sim digital twins. However, robot-mounted cameras naturally rotate during manipulation and navigation, while learned single-view reconstruction models often rely on view-dependent priors and may generalize poorly to out-of-distribution camera rotations. Such rotations can introduce 3D inconsistencies, incorrect layouts, and violations of physical constraints, but this failure mode remains under-evaluated. We introduce an evaluation protocol with controlled axis-wise roll, pitch, and yaw sweeps to trace errors in monocular depth estimation (MDE), canonical object meshes, camera-space layout, and physical plausibility within a representative SAM3D-style pipeline. On the Aria Digital Twin dataset and a real Franka wrist-camera sequence, camera rotations induce MDE distortion, layout drift, and collision penetration, while canonical mesh predictions remain relatively stable. A two-stage SAM3D+FoundationPose pipeline is more robust than one-stage feed-forward layout prediction, and our Gravity-Aware Refinement reduces one-stage pairwise ICP-based layout-orientation error by 47.1$\%$. Our evaluation reveals that current single-view mesh reconstruction methods generalize poorly to robot camera rotation, and suggests that explicit gravity cues are important for reliable robotic single-view mesh reconstruction.
Related papers
- OmniPoint: Universal Monocular Metric Pointcloud from Any Camera [79.24417597712802]
We present OmniPoint, a unified framework designed to generalize metric reconstruction across diverse imaging sensors.<n>To overcome projection rigidity, OmniPoint abandons conventional planar depth regression.<n>It instead adopts a decoupled ray and distance representation alongside a decoupled training objective.
arXiv Detail & Related papers (2026-09-08T19:45:58Z) - Robo3R: Enhancing Robotic Manipulation with Accurate Feed-Forward 3D Reconstruction [70.06600045165905]
3D spatial perception is fundamental to general robotic manipulation, yet obtaining reliable, high-quality 3D geometry remains challenging.<n>We introduce Robo3R, a manipulation-ready 3D reconstruction model that predicts accurate, metric-scale scene geometry directly from RGB images and robot states in real time.<n>We observe consistent gains in performance, suggesting the promise of this alternative 3D sensing module for robotic manipulation.
arXiv Detail & Related papers (2026-02-10T18:58:15Z) - Fiducial Exoskeletons: Image-Centric Robot State Estimation [21.491677821308688]
We introduce Fiducial Exoskeletons, an image-based reformulation of 3D robot state estimation.<n>Our key insight is twofold. First, we cast robot state estimation as 6D pose estimation of each link from a single RGB image.<n>Second, we make per-link 6D pose estimation robust and simple - even without learning.
arXiv Detail & Related papers (2026-01-12T22:04:25Z) - From Camera to World: A Plug-and-Play Module for Human Mesh Transformation [1.5453237467077674]
We propose Mesh-Plug, a plug-and-play module that transforms human meshes from camera coordinates to world coordinates.<n>Key innovation lies in a human-centered approach that leverages both RGB images and depth maps rendered from the initial mesh to estimate camera rotation parameters.<n>Our framework outperforms state-of-the-art methods on the benchmark datasets SPEC-SYN and SPEC-MTP.
arXiv Detail & Related papers (2025-12-17T09:05:46Z) - Is Single-View Mesh Reconstruction Ready for Robotics? [78.14584238127338]
We evaluate single-view mesh reconstruction models for their potential in enabling instant digital twin creation for real-time planning and dynamics prediction using physics simulators for robotic manipulation.<n>Our findings highlight critical gaps between computer vision advances and robotics needs, guiding future research at this intersection.
arXiv Detail & Related papers (2025-05-23T14:35:56Z) - FreeSplatter: Pose-free Gaussian Splatting for Sparse-view 3D Reconstruction [69.63414788486578]
FreeSplatter is a scalable feed-forward framework that generates high-quality 3D Gaussians from uncalibrated sparse-view images.<n>Our approach employs a streamlined transformer architecture where self-attention blocks facilitate information exchange.<n>We develop two specialized variants--for object-centric and scene-level reconstruction--trained on comprehensive datasets.
arXiv Detail & Related papers (2024-12-12T18:52:53Z) - Neural Scene Representation for Locomotion on Structured Terrain [56.48607865960868]
We propose a learning-based method to reconstruct the local terrain for a mobile robot traversing urban environments.
Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the estimates the topography in the robot's vicinity.
We propose a 3D reconstruction model that faithfully reconstructs the scene, despite the noisy measurements and large amounts of missing data coming from the blind spots of the camera arrangement.
arXiv Detail & Related papers (2022-06-16T10:45:17Z) - MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision [72.5863451123577]
We show how to train a neural model that can perform accurate 3D pose and camera estimation.
Our method outperforms both classical bundle adjustment and weakly-supervised monocular 3D baselines.
arXiv Detail & Related papers (2021-08-10T18:39:56Z) - Reconstruction of 3D flight trajectories from ad-hoc camera networks [19.96488566402593]
We present a method to reconstruct the 3D trajectory of an airborne robotic system only from videos recorded with cameras that are unsynchronized.
Our approach enables robust and accurate outside-in tracking of dynamically flying targets, with cheap and easy-to-deploy equipment.
arXiv Detail & Related papers (2020-03-10T14:57:32Z)
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.