{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:DSLRZ4FCYVNENMIHNIUY63IYUZ","short_pith_number":"pith:DSLRZ4FC","canonical_record":{"source":{"id":"2409.07041","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-11T06:12:26Z","cross_cats_sorted":[],"title_canon_sha256":"69e4e2cda14afd5195fbc8b65ec3ff733f78bbbdebb74c3c3c10356c6111635a","abstract_canon_sha256":"c181e4dcafd642c260f40014c38a9cddca891a72b74cf5f0a04e526f42f0c517"},"schema_version":"1.0"},"canonical_sha256":"1c971cf0a2c55a46b1076a298f6d18a6769fa6df126d4858e48d51120ff1d5bd","source":{"kind":"arxiv","id":"2409.07041","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.07041","created_at":"2026-07-05T10:29:23Z"},{"alias_kind":"arxiv_version","alias_value":"2409.07041v2","created_at":"2026-07-05T10:29:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.07041","created_at":"2026-07-05T10:29:23Z"},{"alias_kind":"pith_short_12","alias_value":"DSLRZ4FCYVNE","created_at":"2026-07-05T10:29:23Z"},{"alias_kind":"pith_short_16","alias_value":"DSLRZ4FCYVNENMIH","created_at":"2026-07-05T10:29:23Z"},{"alias_kind":"pith_short_8","alias_value":"DSLRZ4FC","created_at":"2026-07-05T10:29:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:DSLRZ4FCYVNENMIHNIUY63IYUZ","target":"record","payload":{"canonical_record":{"source":{"id":"2409.07041","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-11T06:12:26Z","cross_cats_sorted":[],"title_canon_sha256":"69e4e2cda14afd5195fbc8b65ec3ff733f78bbbdebb74c3c3c10356c6111635a","abstract_canon_sha256":"c181e4dcafd642c260f40014c38a9cddca891a72b74cf5f0a04e526f42f0c517"},"schema_version":"1.0"},"canonical_sha256":"1c971cf0a2c55a46b1076a298f6d18a6769fa6df126d4858e48d51120ff1d5bd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:29:23.009912Z","signature_b64":"YE5qTdB4sTE0Ifz+/6iZldu3vuV+qQg9AZksEIfbYjr3bWafzqT4VG9MLIBhhmfykpbu3YGYa70Z+aNA9DyxAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c971cf0a2c55a46b1076a298f6d18a6769fa6df126d4858e48d51120ff1d5bd","last_reissued_at":"2026-07-05T10:29:23.008736Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:29:23.008736Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.07041","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:29:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QAgF19PIHbJKRGWW9p1/c81V3Rz01fotbfm4iPZMvunrQrx6CSxC0Uisn283kWWoChPGJRcnS6LbU16EUEzcBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:09:54.457700Z"},"content_sha256":"a02ff9ff4759314d83b027fea0b998c3269eff3ff7813423e2593889e69d4165","schema_version":"1.0","event_id":"sha256:a02ff9ff4759314d83b027fea0b998c3269eff3ff7813423e2593889e69d4165"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:DSLRZ4FCYVNENMIHNIUY63IYUZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SoftShadow: Leveraging Soft Masks for Penumbra-Aware Shadow Removal","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bihan Wen, Lanqing Guo, Siyu Huang, Xinrui Wang, Xiyu Wang","submitted_at":"2024-09-11T06:12:26Z","abstract_excerpt":"Recent advancements in deep learning have yielded promising results for the image shadow removal task. However, most existing methods rely on binary pre-generated shadow masks. The binary nature of such masks could potentially lead to artifacts near the boundary between shadow and non-shadow areas. In view of this, inspired by the physical model of shadow formation, we introduce novel soft shadow masks specifically designed for shadow removal. To achieve such soft masks, we propose a SoftShadow framework by leveraging the prior knowledge of pretrained SAM and integrating physical constraints. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.07041","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.07041/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:29:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ExaaldEakqxtO0ukItcvy9Pd/Kf7UcbctLdOTJJ3ZoMQYP2AwArFOD6fmUx2PBA2BQxhIdExhrMf/WzPFqy9Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:09:54.458078Z"},"content_sha256":"88d89a41a370276efd1eacf7d28afad1f7a2c6b4be5ede34dba82512aed4f0e3","schema_version":"1.0","event_id":"sha256:88d89a41a370276efd1eacf7d28afad1f7a2c6b4be5ede34dba82512aed4f0e3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DSLRZ4FCYVNENMIHNIUY63IYUZ/bundle.json","state_url":"https://pith.science/pith/DSLRZ4FCYVNENMIHNIUY63IYUZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DSLRZ4FCYVNENMIHNIUY63IYUZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T13:09:54Z","links":{"resolver":"https://pith.science/pith/DSLRZ4FCYVNENMIHNIUY63IYUZ","bundle":"https://pith.science/pith/DSLRZ4FCYVNENMIHNIUY63IYUZ/bundle.json","state":"https://pith.science/pith/DSLRZ4FCYVNENMIHNIUY63IYUZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DSLRZ4FCYVNENMIHNIUY63IYUZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:DSLRZ4FCYVNENMIHNIUY63IYUZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c181e4dcafd642c260f40014c38a9cddca891a72b74cf5f0a04e526f42f0c517","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-11T06:12:26Z","title_canon_sha256":"69e4e2cda14afd5195fbc8b65ec3ff733f78bbbdebb74c3c3c10356c6111635a"},"schema_version":"1.0","source":{"id":"2409.07041","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.07041","created_at":"2026-07-05T10:29:23Z"},{"alias_kind":"arxiv_version","alias_value":"2409.07041v2","created_at":"2026-07-05T10:29:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.07041","created_at":"2026-07-05T10:29:23Z"},{"alias_kind":"pith_short_12","alias_value":"DSLRZ4FCYVNE","created_at":"2026-07-05T10:29:23Z"},{"alias_kind":"pith_short_16","alias_value":"DSLRZ4FCYVNENMIH","created_at":"2026-07-05T10:29:23Z"},{"alias_kind":"pith_short_8","alias_value":"DSLRZ4FC","created_at":"2026-07-05T10:29:23Z"}],"graph_snapshots":[{"event_id":"sha256:88d89a41a370276efd1eacf7d28afad1f7a2c6b4be5ede34dba82512aed4f0e3","target":"graph","created_at":"2026-07-05T10:29:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2409.07041/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in deep learning have yielded promising results for the image shadow removal task. However, most existing methods rely on binary pre-generated shadow masks. The binary nature of such masks could potentially lead to artifacts near the boundary between shadow and non-shadow areas. In view of this, inspired by the physical model of shadow formation, we introduce novel soft shadow masks specifically designed for shadow removal. To achieve such soft masks, we propose a SoftShadow framework by leveraging the prior knowledge of pretrained SAM and integrating physical constraints. ","authors_text":"Bihan Wen, Lanqing Guo, Siyu Huang, Xinrui Wang, Xiyu Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-11T06:12:26Z","title":"SoftShadow: Leveraging Soft Masks for Penumbra-Aware Shadow Removal"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.07041","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a02ff9ff4759314d83b027fea0b998c3269eff3ff7813423e2593889e69d4165","target":"record","created_at":"2026-07-05T10:29:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c181e4dcafd642c260f40014c38a9cddca891a72b74cf5f0a04e526f42f0c517","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-09-11T06:12:26Z","title_canon_sha256":"69e4e2cda14afd5195fbc8b65ec3ff733f78bbbdebb74c3c3c10356c6111635a"},"schema_version":"1.0","source":{"id":"2409.07041","kind":"arxiv","version":2}},"canonical_sha256":"1c971cf0a2c55a46b1076a298f6d18a6769fa6df126d4858e48d51120ff1d5bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c971cf0a2c55a46b1076a298f6d18a6769fa6df126d4858e48d51120ff1d5bd","first_computed_at":"2026-07-05T10:29:23.008736Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:29:23.008736Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YE5qTdB4sTE0Ifz+/6iZldu3vuV+qQg9AZksEIfbYjr3bWafzqT4VG9MLIBhhmfykpbu3YGYa70Z+aNA9DyxAg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:29:23.009912Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.07041","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a02ff9ff4759314d83b027fea0b998c3269eff3ff7813423e2593889e69d4165","sha256:88d89a41a370276efd1eacf7d28afad1f7a2c6b4be5ede34dba82512aed4f0e3"],"state_sha256":"cb684763ccc5303ecd3cb304c57bb016a7bfb423cd3cd850de6fce45964a09dc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DAZ3rvf6Q/KYBvnG/2eVNm7pqPPwQ4cDvCNTmLQlSNnYh2Pv+fFTR6NxeqWcLGhzpkupYtaw2RqkWTBbW7BRDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T13:09:54.460218Z","bundle_sha256":"87f22a689be095547235c97244a4d79f428c02214480be4dab8ffa2dfc409830"}}