Computational geometry: an introduction
Computational geometry: an introduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Three-Dimensional Surface Reconstruction Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volumetric shape description of range data using “Blobby Model”
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Describing Complicated Objects by Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
Zippered polygon meshes from range images
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
Optimal Registration of Object Views Using Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Programming Fitting of Implicit Polynomials
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
The digital Michelangelo project: 3D scanning of large statues
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Indoor scene reconstruction from sets of noisy range images
Graphical Models
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Registering Multiview Range Data to Create 3D Computer Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Recovering Hyperquadrics from Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable Surface Reconstructiuon from Multiple Range Images
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Adaptive shape evolution using blending
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Self-calibration of a light striping system by matching multiple 3-D profile maps
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Faithful recovering of quadric surfaces from 3D range data
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Higher-Order Nonlinear Priors for Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum-Likelihood Registration of Range Images with Missing Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper describes how to estimate 3D surface models from dense sets of noisy range data taken from different points of view, i.e., multiple range maps. The proposed method uses a sensor model to develop an expression for the likelihood of a 3D surface, conditional on a set of noisy range measurements. Optimizing this likelihood with respect to the model parameters provides an unbiased and efficient estimator. The proposed numerical algorithms make this estimation computationally practical for a wide variety of circumstances. The results from this method compare favorably with state-of-the-art approaches that rely on the closest-point or perpendicular distance metric, a convenient heuristic that produces biased solutions and fails completely when surfaces are not sufficiently smooth, as in the case of complex scenes or noisy range measurements. Empirical results on both simulated and real ladar data demonstrate the effectiveness of the proposed method for several different types of problems. Furthermore, the proposed method offers a general framework that can accommodate extensions to include surface priors (i.e., maximum a posteriori), more sophisticated noise models, and other sensing modalities, such as sonar or synthetic aperture radar.