| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_a68790a12228f5b99e176e8aacff640a |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10016
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2200-12 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-044
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-002
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate |
2019-12-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_046be4256184fd4a4cab56c7128718a5
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7bc313fbfa0910a481cad1af4b3c90a8
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4d8dbf35ee4c8852a9bc85a3f2d6373b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_99111a1f5d66f279200141f6bed710e8
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_40bf7124ff63d85d4d205a00ab6070c0
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_15772e07a3888542e5a566b93ec11a69 |
| publicationDate |
2020-04-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2020126192-A1 |
| titleOfInvention |
Neural network system with temporal feedback for denoising of rendered sequences |
| abstract |
A neural network-based rendering technique increases temporal stability and image fidelity of low sample count path tracing by optimizing a distribution of samples for rendering each image in a sequence. A sample predictor neural network learns spatio-temporal sampling strategies such as placing more samples in dis-occluded regions and tracking specular highlights. Temporal feedback enables a denoiser neural network to boost the effective input sample count and increases temporal stability. The initial uniform sampling step typically present in adaptive sampling algorithms is not needed. The sample predictor and denoiser operate at interactive rates to achieve significantly improved image quality and temporal stability compared with conventional adaptive sampling techniques. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11532073-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022051414-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-10846593-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11126895-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113592699-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2022036015-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112308028-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11540798-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112927159-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11067786-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/GB-2602899-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2023004727-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11645761-B2 |
| priorityDate |
2017-07-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |