HuSc3D: Human Sculpture dataset for 3D object reconstruction

TL;DR

HuSc3D is a new dataset designed to evaluate 3D reconstruction models in realistic, challenging scenarios with detailed sculptures, variable image counts, and real-world acquisition issues, revealing model sensitivities.

Contribution

The paper introduces HuSc3D, a dataset that captures real-world complexities for benchmarking 3D reconstruction, emphasizing detailed sculptures and variable data conditions.

Findings

Existing methods show varied performance on HuSc3D.

Model sensitivity to geometric details and color ambiguity is highlighted.

Limited training data impacts reconstruction quality.

Abstract

3D scene reconstruction from 2D images is one of the most important tasks in computer graphics. Unfortunately, existing datasets and benchmarks concentrate on idealized synthetic or meticulously captured realistic data. Such benchmarks fail to convey the inherent complexities encountered in newly acquired real-world scenes. In such scenes especially those acquired outside, the background is often dynamic, and by popular usage of cell phone cameras, there might be discrepancies in, e.g., white balance. To address this gap, we present HuSc3D, a novel dataset specifically designed for rigorous benchmarking of 3D reconstruction models under realistic acquisition challenges. Our dataset uniquely features six highly detailed, fully white sculptures characterized by intricate perforations and minimal textural and color variation. Furthermore, the number of images per scene varies significantly, introducing the additional challenge of limited training data for some instances alongside scenes with a standard number of views. By evaluating popular 3D reconstruction methods on this diverse dataset, we demonstrate the distinctiveness of HuSc3D in effectively differentiating model performance, particularly highlighting the sensitivity of methods to fine geometric details, color ambiguity, and varying data availability--limitations often masked by more conventional datasets.