The DOI matches its Crossref record.
Improving noise
Abstract
Two deficiencies in the original Noise algorithm are corrected: second order interpolation discontinuity and unoptimal gradient computation. With these defects corrected, Noise both looks better and runs faster. The latter change also makes it easier to define a uniform mathematical reference standard.
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
Cited by
Collaborative learning in networks
Mason, W., & Watts, D. J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1110069108 · Cited by 356
Virtual character performance from speech
Marsella, S., Xu, Y., Lhommet, M., Feng, A., Scherer, S., & Shapiro, A. (2013) · Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation · doi:10.1145/2485895.2485900 · Cited by 168
Robot Learning From Randomized Simulations: A Review
Muratore, F., Ramos, F., Turk, G., Yu, W., Gienger, M., & Peters, J. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.799893 · Cited by 128
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
Sundaresan, P., Grannen, J., Thananjeyan, B., Balakrishna, A., Laskey, M., Stone, K., Gonzalez, J. E., & Goldberg, K. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197121 · Cited by 107
Guided attention network for object detection and counting on drones
YuanQiang, C., Du, D., Zhang, L., Wen, L., Wang, W., Wu, Y., & Lyu, S. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413816 · Cited by 58
Metadata
- Authors
- Perlin, K.
- Venue
- ACM Transactions on Graphics
- Published
- 2002-07
- Volume
- 21
- Issue
- 3
- Pages
- 681-682
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W4239515185
- Cited by
- 260
- References
- 0
- FWCI
- 2.45
- Type
- journal article
- 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
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.
