This paper presents a critical analysis of image-based 3D reconstruction using neural radiance fields (NeRFs), with a focus on quantitative comparisons with respect to traditional photogrammetry. The aim is, therefore, to objectively evaluate the strengths and weaknesses of NeRFs and provide insights into their applicability to different real-life scenarios, from small objects to heritage and industrial scenes. After a comprehensive overview of photogrammetry and NeRF methods, highlighting their respective advantages and disadvantages, various NeRF methods are compared using diverse objects with varying sizes and surface characteristics, including texture-less, metallic, translucent, and transparent surfaces. We evaluated the quality of the resulting 3D reconstructions using multiple criteria, such as noise level, geometric accuracy, and the number of required images (i.e., image baselines). The results show that NeRFs exhibit superior performance over photogrammetry in terms of non-collaborative objects with texture-less, reflective, and refractive surfaces. Conversely, photogrammetry outperforms NeRFs in cases where the object’s surface possesses cooperative texture. Such complementarity should be further exploited in future works.
Abstract. Airborne LiDAR technology has become an essential tool in archaeology during the last two decades since it allows archaeologists to measure and map items or structures that would otherwise be hidden under vegetation. In order to detect and characterise the archaeological evidence, it is a common practice to extract accurate digital terrain models (DTM) by filtering out the vegetation from Airborne Laser Scanning (ALS) datasets. Although previous approaches have performed well in ALS filtration, they are still subject to several variables (flight height, forest cover, type of sensors utilised, etc.) and are frequently integrated into expensive commercial software or customised for specific locations. This study presents a workflow for treating ALS archaeological datasets using machine learning algorithms for both filtering the vegetation and detecting hidden structures. The workflow is applied to two different archaeological environments (in terms of structures, vegetation, landscape, point density), and results demonstrate that the pipeline is rapid and accurate, and the prediction model is transferable.
Abstract. Conventional or learning-based 3D reconstruction methods from images have clearly shown their potential for 3D heritage documentation. Nevertheless, Neural Radiance Field (NeRF) approaches are recently revolutionising the way a scene can be rendered or reconstructed in 3D from a set of oriented images. Therefore the paper wants to review some of the last NeRF methods applied to various cultural heritage datasets collected with smartphone videos, touristic approaches or reflex cameras. Firstly several NeRF methods are evaluated. It turned out that Instant-NGP and Nerfacto methods achieved the best outcomes, outperforming all other methods significantly. Successively qualitative and quantitative analyses are performed on various datasets, revealing the good performances of NeRF methods, in particular for areas with uniform texture or shining surfaces, as well as for small datasets of lost artefacts. This is for sure opening new frontiers for 3D documentation, visualization and communication purposes of digital heritage.
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