HuSc3D: Human Sculpture Dataset for 3D Object Reconstruction
Abstract 3D scene reconstruction from 2D images is 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 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 features six highly detailed, fully white sculptures characterized by intricate perforations and minimal textural 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 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.
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MeshSplats: Mesh-Based Rendering with Gaussian Splatting Initialization
Abstract Gaussian Splatting (GS) is an emerging, pivotal technique in 3D computer graphics. Most GS-based algorithms exclude the use of classical methods such as ray tracing, which offer numerous inherent advantages in rendering. For example, ray tracing can handle incoherent rays for advanced lighting effects, including shadows and reflections. To address this issue, we introduce MeshSplats, a method which converts GS to a mesh-like format. Following the completion of training, MeshSplats transforms Gaussian elements into mesh faces, enabling rendering using ray tracing methods with all their associated benefits. Our model can be used immediately after transformation, yielding a mesh without additional training, with only a slight reduction in reconstruction quality. We can enhance the quality by applying a dedicated optimization algorithm that operates on mesh faces rather than Gaussian components. Importantly, MeshSplats acts as a wrapper, converting pre-trained GS models into a ray-traceable format. The efficacy of our method is substantiated by experimental results, underscoring its extensive applications in computer graphics and image processing.
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