ArXiv

Plenoptic Condensation: A Novel Approach to Generalized Scene Reconstruction

2026-07-20 · 16:50 UTC ·Brevin Tilmon, Alex DeJournett, John Leffingwell... ·1 min read
Authors
Brevin Tilmon, Alex DeJournett, John Leffingwell...
Categories
cs.CV
arXiv
https://arxiv.org/abs/2607.18151v1
PDF
https://arxiv.org/pdf/2607.18151v1

Brief

Plenoptic Condensation (PCon) is a generalized scene reconstruction method that converts images into low-power 'soupy' elements and adaptively condenses them into high-power Reality Models (Relms) with spatially varying representational power. PCon delivers high-fidelity rendering and precise measurement — on a 'Damaged Fiat' benchmark it outperforms NeRO and RT-Splatting by over 2×, achieving a 35 μm local-damage error. Only the abstract was available for this summary.

Why it matters

Plenoptic Condensation (PCon) introduces Reality Models (Relms) that convert images into low-power 'soupy' scene elements and adaptively condense them into higher-power structured elements, enabling spatially varying representational power for sharp edges and smooth reflective surfaces.

Key details

  • On the 'Damaged Fiat' benchmark (paper posted 2026-07-20), PCon reconstructs the car hood more than twice as accurately as NeRO and RT-Splatting and reports a local damage profile error of 35 μm (0.035 mm), while the other SOTA methods were essentially unable to measure the damage.
  • Authors demonstrate in-the-wild reconstructions from consumer phone cameras and drones; project page: https://quidient.github.io/pcon-2026.html.
Source evidence

Abstract

We present a novel Generalized Scene Reconstruction (GSR) approach called Plenoptic Condensation (PCon). PCon uses a multi-stage reconstruction pipeline, initially converting images into "soupy" scene elements with low (representational) power, then adaptively condensing the "soup" into "structured" elements of higher power capable of efficiently representing, for example, sharp edges and smooth reflective surfaces. PCon scene models called Reality Models (Relms) enable spatially varying representational power, which is essential for high-fidelity rendering, measurement, and scene understanding. We showcase several in-the-wild PCon reconstructions captured with consumer phone cameras and drones. In one case called "Damaged Fiat", PCon is benchmarked against two state-of-the-art (SOTA) GSR methods: NeRO and RT-Splatting. Referring to Figure 1 below, PCon reconstructs the car hood more than twice as accurately as the SOTA methods. But more importantly, the local damage profile error for PCon is 35 um (0.035 mm), whereas the two other SOTA methods are essentially unable to measure the damage at all. Our project website is available at https://quidient.github.io/pcon-2026.html.