{"id":"W4402727731","doi":"10.1109/cvpr52733.2024.01985","title":"PAPR in Motion: Seamless Point-level 3D Scene Interpolation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpolation (computer graphics); Computer science; Computer vision; Motion (physics); Computer graphics (images); Point (geometry); Artificial intelligence; Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002315813,0.00009570169,0.00008337109,0.0001996773,0.00003218859,0.0002854436,0.000418797,0.00004157321,0.00003252165],"category_scores_gemma":[0.00003587529,0.00008566914,0.00002344284,0.000575195,0.00002425149,0.001729276,0.0002261221,0.000144499,0.00005681933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007832317,"about_ca_system_score_gemma":0.00004607563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001847158,"about_ca_topic_score_gemma":0.00001446676,"domain_scores_codex":[0.9991468,0.00002539298,0.0001796924,0.0003410193,0.0001427818,0.0001643174],"domain_scores_gemma":[0.9995751,0.00003977375,0.00002240883,0.0002813125,0.00004906315,0.000032368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002661414,0.00004361296,0.0003532961,0.00008357534,0.000004168395,0.00004875656,0.0006584711,0.00001717856,0.009094915,0.07303527,0.001455711,0.9152024],"study_design_scores_gemma":[0.00006159221,0.00001960829,0.0006002355,0.0001745262,0.000001015235,0.00002214908,0.00001355982,0.9207246,0.008874879,0.06855446,0.0008145719,0.0001387684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001709859,0.0002429202,0.9909724,0.00185496,0.0002039103,0.00008374423,7.655212e-7,0.001229399,0.005240926],"genre_scores_gemma":[0.3428276,0.000007713226,0.6564114,0.0002020983,0.00002728874,0.00001097653,0.000001254641,0.000007103151,0.0005045478],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9207075,"threshold_uncertainty_score":0.3493486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02930357247357336,"score_gpt":0.3050992219203529,"score_spread":0.2757956494467796,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}