SNeS: Learning Probably Symmetric Neural Surfaces from Incomplete Data

نویسندگان

چکیده

We present a method for the accurate 3D reconstruction of partly-symmetric objects. build on strengths recent advances in neural and rendering such as Neural Radiance Fields (NeRF). A major shortcoming approaches is that they fail to reconstruct any part object which not clearly visible training image, often case in-the-wild images videos. When evidence lacking, structural priors symmetry can be used complete missing information. However, exploiting highly non-trivial: while geometry non-reflective materials may symmetric, shadows reflections from ambient scene are symmetric general. To address this, we apply soft constraint material properties, having factored appearance into lighting, albedo colour reflectivity. evaluate our recently introduced CO3D dataset, focusing car category due challenge reconstructing highly-reflective materials. show it unobserved regions with high fidelity render high-quality novel view images.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2022

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-031-19824-3_22