{"id":"W4416139122","doi":"10.48550/arxiv.2506.05347","title":"Neural Inverse Rendering from Propagating Light","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Rendering (computer graphics); Radiance; Inverse; Artificial neural network; Inverse problem; Lidar; Visualization; Real-time rendering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002138889,0.0003209976,0.0003336292,0.0002796811,0.0001671348,0.0003432136,0.00180289,0.0002331789,0.000009825907],"category_scores_gemma":[0.00005141923,0.0003288196,0.0001760449,0.0004595542,0.00002917071,0.0002559897,0.004779608,0.000635205,0.00001177723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005717083,"about_ca_system_score_gemma":0.0001507323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002742855,"about_ca_topic_score_gemma":0.00003987071,"domain_scores_codex":[0.9979336,0.0001122777,0.0004599517,0.0009426013,0.00026773,0.0002838268],"domain_scores_gemma":[0.9981166,0.00007183613,0.0002803972,0.001282942,0.0001548564,0.00009332689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001701423,0.0004452039,0.7292585,0.0009994522,0.0005207853,0.0002394343,0.01021156,0.001099233,0.004059021,0.1615699,0.01723019,0.07434963],"study_design_scores_gemma":[0.0002787771,0.00005991916,0.025578,0.0009278544,0.00003583105,0.000003095437,0.00002511207,0.9136019,0.02892579,0.02240822,0.007176054,0.0009794292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3249196,0.0001877864,0.6697757,0.0008207802,0.001634699,0.0003297966,0.00001104645,0.001342239,0.0009782973],"genre_scores_gemma":[0.9669178,0.00009380961,0.0311694,0.001073794,0.0003253101,0.00007376105,0.0000431065,0.00002139656,0.0002816281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9125027,"threshold_uncertainty_score":0.9999164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05575201891622753,"score_gpt":0.3026008314466782,"score_spread":0.2468488125304507,"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."}}