Semantic Segmentation of Urban Textured Meshes Through Point Sampling
Textured meshes are becoming an increasingly popular representation combining the 3D geometry and radiometry of real scenes. However, semantic segmentation algorithms for urban mesh have been little investigated and do not exploit all radiometric information. To address this problem, we adopt an approach consisting in sampling a point cloud from the textured mesh, then using a point cloud semantic segmentation algorithm on this cloud, and finally using the obtained semantic to segment the initial mesh. In this paper, we study the influence of different parameters such as the sampling method, the density of the extracted cloud, the features selected (color, normal, elevation) as well as the number of points used at each training period. Our result outperforms the state-of-the-art on the SUM dataset, earning about 4 points in OA and 18 points in mIoU.
Code (0)
등록된 구현이 없습니다.
Tasks
3D geometrySegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes
Semantic segmentation in urban scene analysis has mainly focused on images or point clouds, while textured meshes - offering richer spatial representation - remain underexplored. This paper introduces SUM Parts, the firs…
3D Semantic SegmentationBenchmarkingSegmentationSemantic SegmentationSemantic Segmentation of Textured Non-manifold 3D Meshes using Transformers
Textured 3D meshes jointly represent geometry, topology, and appearance, yet their irregular structure poses significant challenges for deep-learning-based semantic segmentation. While a few recent methods operate direct…
Semantic SegmentationPSSNet: Planarity-sensible Semantic Segmentation of Large-scale Urban Meshes
We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with the boundaries of planar regions, our f…
SegmentationSemantic SegmentationLMSeg: A deep graph message-passing network for efficient and accurate semantic segmentation of large-scale 3D landscape meshes
Semantic segmentation of large-scale 3D landscape meshes is pivotal for various geospatial applications, including spatial analysis, automatic mapping and localization of target objects, and urban planning and developmen…
Computational EfficiencySegmentationSemantic SegmentationSUM: A Benchmark Dataset of Semantic Urban Meshes
Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications l…
3D Semantic SegmentationSegmentationSemantic Segmentation