Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.21090

RoboCloth: A Large-Scale Real Cloth Material Dataset for Neural Reflectance Reconstruction

Zhen Li·Haoran Qin·Francesco Di Sario·Daniel Rebain·Andrea Tagliasacchi
View original source ↗Read source document ↗Export metadata ↓

Abstract

We present RoboCloth, a dataset of 500 real cloth materials captured under flexible, calibrated camera and lighting configurations, where both are robotically controlled during acquisition. Each material is imaged in approximately 580 high-dynamic range frames spanning a wide view-light hemisphere; camera poses are refined via structure-from-motion and aligned to the robot frame, yielding per-material sparse surface geometry and structured per-point reflectance observations ready for neural material training. We demonstrate the value of this data through a two-stage neural reconstruction pipeline: Stage 1 trains a shared MLP decoder on our corpus to learn cloth material priors; Stage 2 then freezes the decoder and fits a dense 2048x2048 latent texture to each target material. Evaluating on held-out materials from our own dataset and on the independent UBO2014 benchmark, we show that a decoder trained on our captures outperforms decoders trained on existing measured reflectance datasets and analytic PBR fitting, transferring to unseen acquisition systems without modification. The dataset, calibration files, observation tensors, and train/test splits are released under CC BY 4.0 on the Hugging Face Hub.

Explore related subjects

cs.CVcs.GR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhen Li, Haoran Qin, Francesco Di Sario, Daniel Rebain, Andrea Tagliasacchi. 2026-10-15. RoboCloth: A Large-Scale Real Cloth Material Dataset for Neural Reflectance Reconstruction. https://arxiv.org/abs/2610.21090

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Text-to-Image Synthesis: A Decade Survey

Text-to-image synthesis (T2I) focuses on generating high-quality images from textual descriptions In this survey, we review 490 papers on T2I spanning the past decade, with representative advances covered through 2026. We start by briefly introducing how GANs, autoregressive models, and diffusion models have been used for image generation, and we additionally examine the shared components whose evolution has driven recent progress. Building on this foundation, we discuss the development of these models for T2I and organize cutting-edge research by problem rather than by chronology alone, covering controllability, personalized and identity-preserving generation, instruction-based image editing, preference alignment and post-training with human and AI feedback, efficiency, safety, and consistency in content and spatial relationships. We further review unified multimodal understanding-and-generation models, summarize the datasets and evaluation metrics commonly used in T2I research, and provide a comparative discussion of the three paradigms together with their respective trade-offs. Finally, we discuss the potential applications of T2I within AIGC, along with the challenges and forward-looking research opportunities in this field.

cs.CV↗
arXiv2026

Towards Accurate State Estimation: Motion Dynamics Kalman Filter for 3D Multi-Object Tracking

Robust 3D state estimation in multi-object tracking is crucial for object localization in self-driving vehicles. Misalignment between the Kalman filter (KF) motion model and an object's motion can cause state drift, particularly in distant and occlusion cases. The static motion model assumption in the conventional KF is widely used in 3D MOT due to its low latency; however, its estimation accuracy degrades as the object's motion disaligns with the chosen model due to traffic. Although recent work introduces a multimodel KF that runs multiple motion models simultaneously to improve state robustness, it incurs significant computational overhead, so many studies still favor the constant-motion KF to maintain efficiency. This work addresses this trade-off by introducing a motion dynamics Kalman filter (MD-KF) that accounts for motion variation through temporal modeling of object motion and adaptively scales the contribution of the motion components in the motion model to correct state-prediction drift caused by this variation, while maintaining computational efficiency by retaining a single-model structure. Evaluations on KITTI and the Waymo Open Dataset, complemented by vehicle experiments, show that MD-KF achieves tracking performance comparable to multimodel KF with an average 1.72x speedup, while outperforming it in handling highly distant and prolonged occluded objects.

cs.CV↗
arXiv2026

RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction

Reconstructing high-fidelity underwater scenes remains a challenging task due to light absorption, scattering, and limited visibility inherent in aquatic environments. This paper presents an enhanced Gaussian Splatting-based framework that improves both the visual quality and geometric accuracy of deep underwater rendering. We propose decoupled learning for RGB channels, guided by the physics of underwater attenuation, to enable more accurate colour restoration. To address sparse-view limitations and improve view consistency, we introduce a frame interpolation strategy with a novel adaptive weighting scheme. Additionally, we introduce a new loss function aimed at reducing noise while preserving edges, which is essential for deep-sea content. We also release a newly collected dataset, Submerged3D, captured specifically in deep-sea environments. Experimental results demonstrate that our framework consistently outperforms state-of-the-art methods with PSNR gains up to 1.90dB, delivering superior perceptual quality and robustness, and offering promising directions for marine robotics and underwater visual analytics. The code of RUSplatting is available at https://github.com/theflash987/RUSplatting and the dataset Submerged3D can be downloaded at https://zenodo.org/records/15482420.

cs.CV↗