RIS-Assisted 3D Spherical Splatting for Object Composition Visualization using Detection Transformers

TL;DR

This paper presents a novel RF-based 3D object reconstruction framework that leverages RIS-enabled field synthesis and Detection Transformers to accurately infer geometry and material composition, advancing immersive multimedia experiences.

Contribution

It introduces a PWE-driven RF framework combining RIS technology with DETR for material-aware 3D object visualization, a novel integration in this domain.

Findings

Achieved 79.35% accuracy in geometry and material classification.

Demonstrated effective RIS-enabled RF field synthesis for object reconstruction.

Validated the approach through simulation results.

Abstract

The pursuit of immersive and structurally aware multimedia experiences has intensified interest in sensing modalities that reconstruct objects beyond the limits of visible light. Conventional optical pipelines degrade under occlusion or low illumination, motivating the use of radio-frequency (RF) sensing, whose electromagnetic waves penetrate materials and encode both geometric and compositional information. Yet, uncontrolled multipath propagation restricts reconstruction accuracy. Recent advances in Programmable Wireless Environments (PWEs) mitigate this limitation by enabling software-defined manipulation of propagation through Reconfigurable Intelligent Surfaces (RISs), thereby providing controllable illumination diversity. Building on this capability, this work introduces a PWE-driven RF framework for three-dimensional object reconstruction using material-aware spherical primitives. The proposed approach combines RIS-enabled field synthesis with a Detection Transformer (DETR) that infers spatial and material parameters directly from extracted RF features. Simulation results confirm the framework's ability to approximate object geometries and classify material composition with an overall accuracy of 79.35%, marking an initial step toward programmable and physically grounded RF-based 3D object composition visualization.