arXiv subjects

Francesco Di Sario

Publications and source records attributed to Francesco Di Sario.

6 recordsLinked to original sources

arXiv2026

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

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.

cs.CV↗

arXiv2026

Spherical Voronoi: Directional Appearance as a Differentiable Partition of the Sphere

Radiance field methods (e.g. 3D Gaussian Splatting) have emerged as a powerful paradigm for novel view synthesis, yet their appearance modeling often relies on Spherical Harmonics (SH), which impose fundamental limitations. SH struggle with high-frequency signals, exhibit Gibbs ringing artifacts, and fail to capture specular reflections - a key component of realistic rendering. Although alternatives like spherical Gaussians offer improvements, they add significant optimization complexity. We propose Spherical Voronoi (SV) as a unified framework for appearance representation in 3D Gaussian Splatting. SV partitions the directional domain into learnable regions with smooth boundaries, providing an intuitive and stable parameterization for view-dependent effects. For diffuse appearance, SV achieves competitive results while keeping optimization simpler than existing alternatives. For reflections - where SH fail - we leverage SV as learnable reflection probes, taking reflected directions as input following principles from classical graphics. This formulation attains state-of-the-art results on synthetic and real-world datasets, demonstrating that SV offers a principled, efficient, and general solution for appearance modeling in explicit 3D representations. Project page: https://sphericalvoronoi.github.io/

cs.CV↗

arXiv2026

GoDe: Gaussians on Demand for Progressive Level of Detail and Scalable Compression

Recent progress in compressing explicit radiance field representations, particularly 3D Gaussian Splatting, has substantially reduced memory consumption while improving real-time rendering performance. However, existing approaches remain inherently single-rate: each compression level requires a separately optimized model, yielding a set of fixed operating points rather than a truly scalable representation. This limits deployment in scenarios where memory, bandwidth, or computational budgets vary across devices or over time. We argue that scalability should be an intrinsic property of the representation. We show that trained explicit radiance models exhibit a structured distribution of information, which can be revealed using standard optimization signals available during training. In particular, aggregated gradient sensitivity provides a simple, model-agnostic criterion to organize primitives from coarse structure to finer refinements. Building on this, we introduce GoDe (Gaussians on Demand), a general framework for scalable compression and progressive level-of-detail control, instantiated for 3D Gaussian Splatting. Starting from a single trained model, GoDe reorganizes Gaussian primitives into a fixed progressive hierarchy supporting multiple rate-distortion operating points without retraining or per-level fine-tuning. A single quantization-aware fine-tuning stage ensures consistent behavior across all levels under low-precision storage. Extensive experiments on standard benchmarks and multiple 3D Gaussian Splatting backbones show that GoDe achieves rate-distortion performance comparable to state-of-the-art single-rate methods, while enabling truly scalable compression and adaptive rendering within a unified representation. Project page: https://gaussians-on-demand.github.io

cs.CV↗

arXiv2026

RAVE: Rate-Adaptive Visual Encoding for 3D Gaussian Splatting

Recent advances in neural scene representations have transformed immersive multimedia, with 3D Gaussian Splatting (3DGS) enabling real-time photorealistic rendering. Despite its efficiency, 3DGS suffers from large memory requirements and costly training procedures, motivating efforts toward compression. Existing approaches, however, operate at fixed rates, limiting adaptability to varying bandwidth and device constraints. In this work, we propose a flexible compression scheme for 3DGS that supports interpolation at any rate between predefined bounds. Our method is computationally lightweight, requires no retraining for any rate, and preserves rendering quality across a broad range of operating points. Experiments demonstrate that the approach achieves efficient, high-quality compression while offering dynamic rate control, making it suitable for practical deployment in immersive applications. The code is available at https://github.com/inspiros/RAVE.

cs.CV↗

arXiv2024

Boost Your NeRF: A Model-Agnostic Mixture of Experts Framework for High Quality and Efficient Rendering

Since the introduction of NeRFs, considerable attention has been focused on improving their training and inference times, leading to the development of Fast-NeRFs models. Despite demonstrating impressive rendering speed and quality, the rapid convergence of such models poses challenges for further improving reconstruction quality. Common strategies to improve rendering quality involves augmenting model parameters or increasing the number of sampled points. However, these computationally intensive approaches encounter limitations in achieving significant quality enhancements. This study introduces a model-agnostic framework inspired by Sparsely-Gated Mixture of Experts to enhance rendering quality without escalating computational complexity. Our approach enables specialization in rendering different scene components by employing a mixture of experts with varying resolutions. We present a novel gate formulation designed to maximize expert capabilities and propose a resolution-based routing technique to effectively induce sparsity and decompose scenes. Our work significantly improves reconstruction quality while maintaining competitive performance.

cs.CV↗

arXiv2022

Cloud-based user modeling for social robots: a first attempt

A social robot is an autonomous robot that interact with people by engaging in social emotive behaviors, skills, capacities, and rules attached to its collaborative role. In order to achieve these goals we believe that modeling the interaction with the user and adapt the robot behavior to the user herself are fundamental for its social role. This paper presents our first attempt to integrate user modeling features in social and affective robots. We propose a cloud-based architecture for modeling the user-robot interaction in order to reuse the approach with different kind of social robots.

cs.RO↗