The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Generating antialiased images at low sampling densities
Abstract
Ray tracing produces point samples of an image from a 3-D model. Constructing an antialiased digital picture from point samples is difficult without resorting to extremely high sampling densities. This paper describes a program that focuses on that problem. While it is impossible to eliminate aliasing totally, it has been shown that nonuniform sampling yields aliasing that is less conspicuous to the observer. An algorithm is presented for fast generation of nonuniform sampling patterns that are optimal in some sense. Some regions of an image may require extra sampling to avoid strong aliasing. Deciding where to do extra sampling can be guided by knowledge of how the eye perceives noise as a function of contrast and color. Finally, to generate the digital picture, the image must be reconstructed from the samples and resampled at the display pixel rate. The nonuniformity of the samples complicates this process, and a new nonuniform reconstruction filter is presented which solves this problem efficiently.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Communication in the presence of noise
Shannon, C. E. (1949) · Proceedings of the IRE · doi:10.1109/jrproc.1949.232969 · Cited by 6,539
Communication In The Presence Of Noise
Shannon, C. E. (1998) · Proceedings of the IEEE · doi:10.1109/jproc.1998.659497 · Cited by 4,804
Ripley, B. D. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01615.x · Cited by 2,511
Kajiya, J. T. (1986) · ACM SIGGRAPH Computer Graphics · doi:10.1145/15886.15902 · Cited by 2,238
Scattered data interpolation: tests of some methods
Franke, R. (1982) · Mathematics of Computation · doi:10.1090/s0025-5718-1982-0637296-4 · Cited by 1,895
An improved illumination model for shaded display
Whitted, T. (1980) · Communications of the ACM · doi:10.1145/358876.358882 · Cited by 1,768
Stochastic sampling in computer graphics
Cook, R. L. (1986) · ACM Transactions on Graphics · doi:10.1145/7529.8927 · Cited by 982
Cook, R. L., Porter, T., & Carpenter, L. C. (1984) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/800031.808590 · Cited by 858
Polachek, H. (1960) · Mathematics of Computation · doi:10.1090/s0025-5718-60-99239-5 · Cited by 759
Sampling and reconstruction of wave-number-limited functions in N-dimensional euclidean spaces
Petersen, D. P., & Middleton, D. (1962) · Information and Control · doi:10.1016/s0019-9958(62)90633-2 · Cited by 614
Cited by
Scattered data interpolation with multilevel B-splines
Lee, S., Wolberg, G., & Shin, S. Y. (1997) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/2945.620490 · Cited by 1,046
Reconstruction filters in computer-graphics
Mitchell, D. P., & Netravali, A. N. (1988) · ACM SIGGRAPH Computer Graphics · doi:10.1145/378456.378514 · Cited by 383
Self-citation
Metadata
- Authors
- Mitchell, D. P.
- Venue
- Proceedings of the 14th annual conference on Computer graphics and interactive techniques
- Publisher
- ACM
- Published
- 1987-08
- Pages
- 65-72
- OpenAlex
- W1965744692
- Cited by
- 385
- References
- 19
- FWCI
- 3.51
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Generating antialiased images at low sampling densities. https://app.cochat.ai/papers/10.1145/37401.37410
- The paper
- Mitchell, D. P. (1987). Generating antialiased images at low sampling densities. Proceedings of the 14th annual conference on Computer graphics and interactive techniques. https://doi.org/10.1145/37401.37410
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
