June 10, 2024, 4:48 a.m. | Aarya Patel, Hamid Laga, Ojaswa Sharma

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.04861v1 Announce Type: new
Abstract: Neural implicit representations have emerged as a powerful paradigm for 3D reconstruction. However, despite their success, existing methods fail to capture fine geometric details and thin structures, especially in scenarios where only sparse RGB views of the objects of interest are available. We hypothesize that current methods for learning neural implicit representations from RGB or RGBD images produce 3D surfaces with missing parts and details because they only rely on 0-order differential properties, i.e. the …

3d reconstruction abstract arxiv cs.cv cs.gr fail fidelity functions however normal objects paradigm success surface type

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