SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving Article Swipe
Zhenpei Yang
,
Yuning Chai
,
Dragomir Anguelov
,
Yin Zhou
,
Pei Sun
,
Dumitru Erhan
,
Sean M. Rafferty
,
Henrik Kretzschmar
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2005.03844
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2005.03844
Autonomous driving system development is critically dependent on the ability to replay complex and diverse traffic scenarios in simulation. In such scenarios, the ability to accurately simulate the vehicle sensors such as cameras, lidar or radar is essential. However, current sensor simulators leverage gaming engines such as Unreal or Unity, requiring manual creation of environments, objects and material properties. Such approaches have limited scalability and fail to produce realistic approximations of camera, lidar, and radar data without significant additional work. In this paper, we present a simple yet effective approach to generate realistic scenario sensor data, based only on a limited amount of lidar and camera data collected by an autonomous vehicle. Our approach uses texture-mapped surfels to efficiently reconstruct the scene from an initial vehicle pass or set of passes, preserving rich information about object 3D geometry and appearance, as well as the scene conditions. We then leverage a SurfelGAN network to reconstruct realistic camera images for novel positions and orientations of the self-driving vehicle and moving objects in the scene. We demonstrate our approach on the Waymo Open Dataset and show that it can synthesize realistic camera data for simulated scenarios. We also create a novel dataset that contains cases in which two self-driving vehicles observe the same scene at the same time. We use this dataset to provide additional evaluation and demonstrate the usefulness of our SurfelGAN model.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2005.03844
- https://arxiv.org/pdf/2005.03844
- OA Status
- green
- Cited By
- 4
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3022466818
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3022466818Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2005.03844Digital Object Identifier
- Title
-
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous DrivingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-08Full publication date if available
- Authors
-
Zhenpei Yang, Yuning Chai, Dragomir Anguelov, Yin Zhou, Pei Sun, Dumitru Erhan, Sean M. Rafferty, Henrik KretzschmarList of authors in order
- Landing page
-
https://arxiv.org/abs/2005.03844Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2005.03844Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2005.03844Direct OA link when available
- Concepts
-
Leverage (statistics), Computer science, Lidar, Computer vision, Artificial intelligence, Scalability, Radar, Real-time computing, Remote sensing, Geology, Telecommunications, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3022466818 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2005.03844 |
| ids.doi | https://doi.org/10.48550/arxiv.2005.03844 |
| ids.mag | 3022466818 |
| ids.openalex | https://openalex.org/W3022466818 |
| fwci | 1.50968188 |
| type | preprint |
| title | SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10481 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9976999759674072 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1704 |
| topics[0].subfield.display_name | Computer Graphics and Computer-Aided Design |
| topics[0].display_name | Computer Graphics and Visualization Techniques |
| topics[1].id | https://openalex.org/T11099 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9975000023841858 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2203 |
| topics[1].subfield.display_name | Automotive Engineering |
| topics[1].display_name | Autonomous Vehicle Technology and Safety |
| topics[2].id | https://openalex.org/T10775 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9962000250816345 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Generative Adversarial Networks and Image Synthesis |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C153083717 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8288736343383789 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q6535263 |
| concepts[0].display_name | Leverage (statistics) |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.80863356590271 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C51399673 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6990578174591064 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q504027 |
| concepts[2].display_name | Lidar |
| concepts[3].id | https://openalex.org/C31972630 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6266472935676575 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[3].display_name | Computer vision |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5720232129096985 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C48044578 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5423027873039246 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q727490 |
| concepts[5].display_name | Scalability |
| concepts[6].id | https://openalex.org/C554190296 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4825429320335388 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q47528 |
| concepts[6].display_name | Radar |
| concepts[7].id | https://openalex.org/C79403827 |
| concepts[7].level | 1 |
| concepts[7].score | 0.33345234394073486 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[7].display_name | Real-time computing |
| concepts[8].id | https://openalex.org/C62649853 |
| concepts[8].level | 1 |
| concepts[8].score | 0.15903151035308838 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[8].display_name | Remote sensing |
| concepts[9].id | https://openalex.org/C127313418 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[9].display_name | Geology |
| concepts[10].id | https://openalex.org/C76155785 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[10].display_name | Telecommunications |
| concepts[11].id | https://openalex.org/C77088390 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[11].display_name | Database |
| keywords[0].id | https://openalex.org/keywords/leverage |
| keywords[0].score | 0.8288736343383789 |
| keywords[0].display_name | Leverage (statistics) |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.80863356590271 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/lidar |
| keywords[2].score | 0.6990578174591064 |
| keywords[2].display_name | Lidar |
| keywords[3].id | https://openalex.org/keywords/computer-vision |
| keywords[3].score | 0.6266472935676575 |
| keywords[3].display_name | Computer vision |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5720232129096985 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/scalability |
| keywords[5].score | 0.5423027873039246 |
| keywords[5].display_name | Scalability |
| keywords[6].id | https://openalex.org/keywords/radar |
| keywords[6].score | 0.4825429320335388 |
| keywords[6].display_name | Radar |
| keywords[7].id | https://openalex.org/keywords/real-time-computing |
| keywords[7].score | 0.33345234394073486 |
| keywords[7].display_name | Real-time computing |
| keywords[8].id | https://openalex.org/keywords/remote-sensing |
| keywords[8].score | 0.15903151035308838 |
| keywords[8].display_name | Remote sensing |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2005.03844 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2005.03844 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2005.03844 |
| locations[1].id | doi:10.48550/arxiv.2005.03844 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article-journal |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2005.03844 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5112885155 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Zhenpei Yang |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I86519309 |
| authorships[0].affiliations[0].raw_affiliation_string | UT-Austin |
| authorships[0].institutions[0].id | https://openalex.org/I86519309 |
| authorships[0].institutions[0].ror | https://ror.org/00hj54h04 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I86519309 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | The University of Texas at Austin |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Zhenpei Yang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | UT-Austin |
| authorships[1].author.id | https://openalex.org/A5079961895 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Yuning Chai |
| authorships[1].affiliations[0].raw_affiliation_string | Waymo |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yuning Chai |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Waymo |
| authorships[2].author.id | https://openalex.org/A5081024054 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Dragomir Anguelov |
| authorships[2].affiliations[0].raw_affiliation_string | Waymo |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dragomir Anguelov |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Waymo |
| authorships[3].author.id | https://openalex.org/A5000117252 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-7536-7753 |
| authorships[3].author.display_name | Yin Zhou |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I86519309 |
| authorships[3].affiliations[0].raw_affiliation_string | University of Texas at Austin |
| authorships[3].institutions[0].id | https://openalex.org/I86519309 |
| authorships[3].institutions[0].ror | https://ror.org/00hj54h04 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I86519309 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | The University of Texas at Austin |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yin Zhou |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | University of Texas at Austin |
| authorships[4].author.id | https://openalex.org/A5060736311 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-4651-6227 |
| authorships[4].author.display_name | Pei Sun |
| authorships[4].affiliations[0].raw_affiliation_string | Waymo |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Pei Sun |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Waymo |
| authorships[5].author.id | https://openalex.org/A5084125668 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-7650-1475 |
| authorships[5].author.display_name | Dumitru Erhan |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I1291425158 |
| authorships[5].affiliations[0].raw_affiliation_string | [Google Brain] |
| authorships[5].institutions[0].id | https://openalex.org/I1291425158 |
| authorships[5].institutions[0].ror | https://ror.org/00njsd438 |
| authorships[5].institutions[0].type | company |
| authorships[5].institutions[0].lineage | https://openalex.org/I1291425158, https://openalex.org/I4210128969 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | Google (United States) |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Dumitru Erhan |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | [Google Brain] |
| authorships[6].author.id | https://openalex.org/A5004723883 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-5752-4380 |
| authorships[6].author.display_name | Sean M. Rafferty |
| authorships[6].affiliations[0].raw_affiliation_string | Waymo |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Sean Rafferty |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Waymo |
| authorships[7].author.id | https://openalex.org/A5012355769 |
| authorships[7].author.orcid | |
| authorships[7].author.display_name | Henrik Kretzschmar |
| authorships[7].affiliations[0].raw_affiliation_string | Waymo |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Henrik Kretzschmar |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Waymo |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2005.03844 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10481 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9976999759674072 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1704 |
| primary_topic.subfield.display_name | Computer Graphics and Computer-Aided Design |
| primary_topic.display_name | Computer Graphics and Visualization Techniques |
| related_works | https://openalex.org/W4319317934, https://openalex.org/W2901265155, https://openalex.org/W2956374172, https://openalex.org/W4281783339, https://openalex.org/W4319837668, https://openalex.org/W4308071650, https://openalex.org/W3188333020, https://openalex.org/W1964041166, https://openalex.org/W4390887692, https://openalex.org/W4210818033 |
| cited_by_count | 4 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2021 |
| counts_by_year[1].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2005.03844 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2005.03844 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2005.03844 |
| primary_location.id | pmh:oai:arXiv.org:2005.03844 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2005.03844 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2005.03844 |
| publication_date | 2020-05-08 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W3106250896, https://openalex.org/W3199643754, https://openalex.org/W2980160556, https://openalex.org/W2963858432, https://openalex.org/W2995681297, https://openalex.org/W2099471712, https://openalex.org/W2997643725, https://openalex.org/W3035172746, https://openalex.org/W2964319688, https://openalex.org/W2431874326, https://openalex.org/W2755286543, https://openalex.org/W2963627347, https://openalex.org/W2009422376, https://openalex.org/W2129404737, https://openalex.org/W2962974533, https://openalex.org/W2963073614, https://openalex.org/W2940868402, https://openalex.org/W2765811365, https://openalex.org/W2557465155, https://openalex.org/W2158037930, https://openalex.org/W2194775991, https://openalex.org/W2912798476, https://openalex.org/W2798930779, https://openalex.org/W2034392969, https://openalex.org/W1861492603, https://openalex.org/W2340897893, https://openalex.org/W2008073424, https://openalex.org/W2886748926, https://openalex.org/W2785678896, https://openalex.org/W2951856026, https://openalex.org/W2766527293, https://openalex.org/W2950893734, https://openalex.org/W2949117887, https://openalex.org/W2767621168, https://openalex.org/W2599765304, https://openalex.org/W2783375473, https://openalex.org/W2064381770, https://openalex.org/W1522301498, https://openalex.org/W2774661387, https://openalex.org/W2953127211 |
| referenced_works_count | 40 |
| abstract_inverted_index.a | 85, 99, 149, 196 |
| abstract_inverted_index.3D | 136 |
| abstract_inverted_index.In | 19, 80 |
| abstract_inverted_index.We | 146, 172, 193, 215 |
| abstract_inverted_index.an | 109, 123 |
| abstract_inverted_index.as | 31, 46, 140, 142 |
| abstract_inverted_index.at | 211 |
| abstract_inverted_index.by | 108 |
| abstract_inverted_index.in | 17, 169, 202 |
| abstract_inverted_index.is | 4, 36 |
| abstract_inverted_index.it | 184 |
| abstract_inverted_index.of | 53, 70, 102, 129, 162, 227 |
| abstract_inverted_index.on | 7, 98, 176 |
| abstract_inverted_index.or | 34, 48, 127 |
| abstract_inverted_index.to | 10, 24, 66, 90, 117, 152, 219 |
| abstract_inverted_index.we | 83 |
| abstract_inverted_index.Our | 112 |
| abstract_inverted_index.and | 13, 56, 64, 73, 104, 138, 160, 166, 181, 223 |
| abstract_inverted_index.can | 185 |
| abstract_inverted_index.for | 157, 190 |
| abstract_inverted_index.our | 174, 228 |
| abstract_inverted_index.set | 128 |
| abstract_inverted_index.the | 8, 22, 27, 120, 143, 163, 170, 177, 208, 212, 225 |
| abstract_inverted_index.two | 204 |
| abstract_inverted_index.use | 216 |
| abstract_inverted_index.yet | 87 |
| abstract_inverted_index.Open | 179 |
| abstract_inverted_index.Such | 59 |
| abstract_inverted_index.also | 194 |
| abstract_inverted_index.data | 75, 106, 189 |
| abstract_inverted_index.fail | 65 |
| abstract_inverted_index.from | 122 |
| abstract_inverted_index.have | 61 |
| abstract_inverted_index.only | 97 |
| abstract_inverted_index.pass | 126 |
| abstract_inverted_index.rich | 132 |
| abstract_inverted_index.same | 209, 213 |
| abstract_inverted_index.show | 182 |
| abstract_inverted_index.such | 20, 30, 45 |
| abstract_inverted_index.that | 183, 199 |
| abstract_inverted_index.then | 147 |
| abstract_inverted_index.this | 81, 217 |
| abstract_inverted_index.uses | 114 |
| abstract_inverted_index.well | 141 |
| abstract_inverted_index.Waymo | 178 |
| abstract_inverted_index.about | 134 |
| abstract_inverted_index.based | 96 |
| abstract_inverted_index.cases | 201 |
| abstract_inverted_index.data, | 95 |
| abstract_inverted_index.lidar | 33, 103 |
| abstract_inverted_index.novel | 158, 197 |
| abstract_inverted_index.radar | 35, 74 |
| abstract_inverted_index.scene | 121, 144, 210 |
| abstract_inverted_index.time. | 214 |
| abstract_inverted_index.which | 203 |
| abstract_inverted_index.work. | 79 |
| abstract_inverted_index.Unity, | 49 |
| abstract_inverted_index.Unreal | 47 |
| abstract_inverted_index.amount | 101 |
| abstract_inverted_index.camera | 105, 155, 188 |
| abstract_inverted_index.create | 195 |
| abstract_inverted_index.gaming | 43 |
| abstract_inverted_index.images | 156 |
| abstract_inverted_index.lidar, | 72 |
| abstract_inverted_index.manual | 51 |
| abstract_inverted_index.model. | 230 |
| abstract_inverted_index.moving | 167 |
| abstract_inverted_index.object | 135 |
| abstract_inverted_index.paper, | 82 |
| abstract_inverted_index.replay | 11 |
| abstract_inverted_index.scene. | 171 |
| abstract_inverted_index.sensor | 40, 94 |
| abstract_inverted_index.simple | 86 |
| abstract_inverted_index.system | 2 |
| abstract_inverted_index.Dataset | 180 |
| abstract_inverted_index.ability | 9, 23 |
| abstract_inverted_index.camera, | 71 |
| abstract_inverted_index.complex | 12 |
| abstract_inverted_index.current | 39 |
| abstract_inverted_index.dataset | 198, 218 |
| abstract_inverted_index.diverse | 14 |
| abstract_inverted_index.driving | 1 |
| abstract_inverted_index.engines | 44 |
| abstract_inverted_index.initial | 124 |
| abstract_inverted_index.limited | 62, 100 |
| abstract_inverted_index.network | 151 |
| abstract_inverted_index.objects | 55, 168 |
| abstract_inverted_index.observe | 207 |
| abstract_inverted_index.passes, | 130 |
| abstract_inverted_index.present | 84 |
| abstract_inverted_index.produce | 67 |
| abstract_inverted_index.provide | 220 |
| abstract_inverted_index.sensors | 29 |
| abstract_inverted_index.surfels | 116 |
| abstract_inverted_index.traffic | 15 |
| abstract_inverted_index.vehicle | 28, 125, 165 |
| abstract_inverted_index.without | 76 |
| abstract_inverted_index.However, | 38 |
| abstract_inverted_index.approach | 89, 113, 175 |
| abstract_inverted_index.cameras, | 32 |
| abstract_inverted_index.contains | 200 |
| abstract_inverted_index.creation | 52 |
| abstract_inverted_index.generate | 91 |
| abstract_inverted_index.geometry | 137 |
| abstract_inverted_index.leverage | 42, 148 |
| abstract_inverted_index.material | 57 |
| abstract_inverted_index.scenario | 93 |
| abstract_inverted_index.simulate | 26 |
| abstract_inverted_index.vehicle. | 111 |
| abstract_inverted_index.vehicles | 206 |
| abstract_inverted_index.SurfelGAN | 150, 229 |
| abstract_inverted_index.collected | 107 |
| abstract_inverted_index.dependent | 6 |
| abstract_inverted_index.effective | 88 |
| abstract_inverted_index.positions | 159 |
| abstract_inverted_index.realistic | 68, 92, 154, 187 |
| abstract_inverted_index.requiring | 50 |
| abstract_inverted_index.scenarios | 16 |
| abstract_inverted_index.simulated | 191 |
| abstract_inverted_index.Autonomous | 0 |
| abstract_inverted_index.accurately | 25 |
| abstract_inverted_index.additional | 78, 221 |
| abstract_inverted_index.approaches | 60 |
| abstract_inverted_index.autonomous | 110 |
| abstract_inverted_index.critically | 5 |
| abstract_inverted_index.essential. | 37 |
| abstract_inverted_index.evaluation | 222 |
| abstract_inverted_index.preserving | 131 |
| abstract_inverted_index.scenarios, | 21 |
| abstract_inverted_index.scenarios. | 192 |
| abstract_inverted_index.simulators | 41 |
| abstract_inverted_index.synthesize | 186 |
| abstract_inverted_index.usefulness | 226 |
| abstract_inverted_index.appearance, | 139 |
| abstract_inverted_index.conditions. | 145 |
| abstract_inverted_index.demonstrate | 173, 224 |
| abstract_inverted_index.development | 3 |
| abstract_inverted_index.efficiently | 118 |
| abstract_inverted_index.information | 133 |
| abstract_inverted_index.properties. | 58 |
| abstract_inverted_index.reconstruct | 119, 153 |
| abstract_inverted_index.scalability | 63 |
| abstract_inverted_index.significant | 77 |
| abstract_inverted_index.simulation. | 18 |
| abstract_inverted_index.orientations | 161 |
| abstract_inverted_index.self-driving | 164, 205 |
| abstract_inverted_index.environments, | 54 |
| abstract_inverted_index.approximations | 69 |
| abstract_inverted_index.texture-mapped | 115 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 93 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.8367513 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |