Efficient Sparse Voxel Octrees

By Samuli Laine, Tero Karras. 2010

343 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

OctNet: Learning Deep 3D Representations at High Resolutions (2016, background): “Areas of application include depth fusion [23], image rendering [26] and 3D reconstruction [44].”
KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs (2021, background): “This might be mitigated by using memory-efficient data structures [8, 20] that allow networks to be exclusively instantiated in the vicinity of surfaces.”
Real-time 3D reconstruction at scale using voxel hashing (2013, methodology): “The rendering literature has proposed many alternative hierarchical data structures [Lefebvre et al. 2005; Kraus and Ertl 2002; Laine and Karras 2011; Kämpe et al. 2013; Reichl et al. 2012].”
Multi-Label Semantic 3D Reconstruction Using Voxel Blocks (2016, methodology): “It is possible for instance to use a data adaptive discretization of the space in form of a Delaunay triangulation [15], an octree data structure to store the geometry [18] or voxel hashing [21].”
OctoMap: an efficient probabilistic 3D mapping framework based on octrees (2013, background): “As a data structure, octrees are applied in a variety of applications, most notably in the area of computer graphics for efficient rendering (Botsch et al., 2002; Surmann et al., 2003; Laine and Karras, 2010) and in the field of photogrammetry to store and address large point clouds (GirardeauMontau”
Neural Sparse Voxel Fields (2020, methodology): “We are inspired by classical computer graphics techniques such as the bounding volume hierarchy (BVH, Rubin and Whitted, 1980) and the sparse voxel octree (SVO, Laine and Karras, 2010) which are designed to model the scene in a sparse hierarchical structure for ray tracing acceleration.”
Baking Neural Radiance Fields for Real-Time View Synthesis (2021, unknown): “However, as shown by Laine and Karras [18], sparse voxel grids can be an effective and efﬁcient representation for opaque surfaces.”
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields (2021, background): “In the context of rendering, prefiltering can be thought of as tracing a cone instead of a ray through each pixel [1, 16]: wherever the cone intersects scene content, a precomputed multiscale representation of the scene content (such as a sparse voxel octree [15, 21] or a mipmap [47]) is queried at ”