RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis

Tremblay, Jonathan; Meshry, Moustafa; Evans, A.C.O.; Kautz, Jan; Keller, Alexander; Khamis, Sameh; Loop, Charles; Morrical, Nate; Nagano, Koki; Takikawa, Towaki; Birchfield, Stan

doi:10.48550/arxiv.2205.07058

Cited by 2 publications

(2 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods are mainly used only as reference points of quality. For all evaluations, we use the 10 brick scenes from RTMV dataset [Tremblay et al 2022] which has challenging high-complexity assets. We use the same evaluation scheme and evaluate the results at a 400 × 400 resolution with LPIPS ] (with VGG), SSIM [Wang et al 2004], and PSNR.…”

Section: Table 3 Comparison Between Random Indices and Learned Indicesmentioning

confidence: 99%

Variable Bitrate Neural Fields

Takikawa¹,

Evans²,

Tremblay³

et al. 2022

Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings

Self Cite

View full text Add to dashboard Cite

Section: Table 3 Comparison Between Random Indices and Learned Indicesmentioning

confidence: 99%

Variable Bitrate Neural Fields

Takikawa¹,

Evans²,

Tremblay³

et al. 2022

Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings

Self Cite

View full text Add to dashboard Cite

“…GSO [27] gets clean 3D models with textures via scanning common household items but includes only a limited number of samples. Some works also construct synthetic multi-view datasets [11,33,85,100,107]. Neverthe- less, these datasets have a small scale and category range for special tasks, which limits the robust and generic learning of 3D deep models.…”

Section: Related Workmentioning

confidence: 99%

Trans-ancestral genome-wide association studies of brain imaging phenotypes

Zhang

et al. 2022

Preprint

View full text Add to dashboard Cite

Genome-wide association studies of brain imaging phenotypes are mainly performed in European populations, but other populations are severely under-represented. Here, we conducted Chinese-alone and trans-ancestral genome-wide association studies of 3,414 brain imaging phenotypes in 7,058 Chinese and 33,224 European individuals. We identified 37 novel variant-phenotype associations in Chinese-alone analyses and 459 additional novel associations in trans-ancestral meta-analyses under the thresholds of P < 1.46 × 10− 11 for discovery and P < 0.05 for replication. We pooled genome-wide significant associations for brain imaging phenotypes identified in either single-ancestral or trans-ancestral analyses into 6,361 independent significant associations. These associations were unevenly distributed in the genome and across the brain phenotypic subgroups and demonstrated significant enrichment for nervous system development and signal transduction. We further categorized the 4,890 pooled genome-wide significant associations whose index variants were included in both Chinese and European analyses into 43 ancestry-specific and 3,524 ancestry-shared associations. Loci of the 6,361 pooled genome-wide significant associations for brain imaging phenotypes were shared by 16 brain-related non-imaging traits including cognition, personality, risk behavior, addiction, and neuropsychiatric disorders. Our results provide a valuable catalog of genetic associations for brain imaging phenotypes in diverse populations.

show abstract

RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis

Cited by 2 publications

References 64 publications

Variable Bitrate Neural Fields

Variable Bitrate Neural Fields

Trans-ancestral genome-wide association studies of brain imaging phenotypes

Contact Info

Product

Resources

About