Change Detection in a 3-d World

By Thomas B. Pollard, J. Mundy. 2007

132 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials (2018, methodology): “Ray-based 3D Reconstruction: Pollard and Mundy [23] propose a volumetric reconstruction method that updates the occupancy and color of each voxel sequentially for every image.”
Learning a Multi-View Stereo Machine (2017, background): “[39, 35, 54] directly reason about viewing rays in a voxel grid, while [34] recovers a quasi dense point cloud.”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “More recent approaches use a probability map defined at regular voxel locations to encode the probability of occupancy (Bhotika et al. (2002); Pollard & Mundy (2007); Ulusoy et al. (2015)).”
Robust monocular SLAM in dynamic environments (2013, background): “Pollard and Mundy [29] represent the target 3D space in a bounded volume partitioned into voxels, and each voxel contains one surface probability and one color model.”
Towards Probabilistic Volumetric Reconstruction Using Ray Potentials (2015, methodology): “Similar online algorithms with a Bayesian interpretation have been proposed in [23, 35].”
Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels (2017, background): “Early probabilistic extensions of their approach include [1, 5, 27].”
Patches, Planes and Probabilities: A Non-Local Prior for Volumetric 3D Reconstruction (2016, methodology): “Finally, we include a comparison to Pollard and Mundy’s approach (“PM”) [37], which lacks a global probabilistic formulation but nevertheless achieves very good results on several datasets [9, 49], including the Middlebury benchmark [44].”
Semantic 3D Reconstruction with Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint (2016, background): “This was formulated in [25, 9, 21, 22, 31] as a problem of finding a voxel labeling in terms of color and occupancy such that the first occupied voxel along a ray has a similar color as the pixel it projects to.”
Ray Markov Random Fields for image-based 3D modeling: Model and efficient inference (2010, methodology): “, occlusion) based on initial surface [19] or currently estimated surface [6, 12]; The other approach estimates the visibility based on the probabilistic opacity of the volume (usually for voxel based approach) [4, 15].”