{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:OXD7HQK27HPW4D2ATZPYDEKX64","short_pith_number":"pith:OXD7HQK2","canonical_record":{"source":{"id":"1511.02300","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-07T04:34:18Z","cross_cats_sorted":[],"title_canon_sha256":"f44706395c1257cb113a0592b0d755b4c8d92c995c51f605b537b08cc2aba638","abstract_canon_sha256":"64f7d78d2f6ab67ddf921f2b6f1d243ff110f72e24c6e4eb2b0fbc03de1cf356"},"schema_version":"1.0"},"canonical_sha256":"75c7f3c15af9df6e0f409e5f819157f72a20dc5b9c513343ef292d71795a810b","source":{"kind":"arxiv","id":"1511.02300","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.02300","created_at":"2026-05-18T01:19:20Z"},{"alias_kind":"arxiv_version","alias_value":"1511.02300v2","created_at":"2026-05-18T01:19:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.02300","created_at":"2026-05-18T01:19:20Z"},{"alias_kind":"pith_short_12","alias_value":"OXD7HQK27HPW","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"OXD7HQK27HPW4D2A","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"OXD7HQK2","created_at":"2026-05-18T12:29:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:OXD7HQK27HPW4D2ATZPYDEKX64","target":"record","payload":{"canonical_record":{"source":{"id":"1511.02300","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-07T04:34:18Z","cross_cats_sorted":[],"title_canon_sha256":"f44706395c1257cb113a0592b0d755b4c8d92c995c51f605b537b08cc2aba638","abstract_canon_sha256":"64f7d78d2f6ab67ddf921f2b6f1d243ff110f72e24c6e4eb2b0fbc03de1cf356"},"schema_version":"1.0"},"canonical_sha256":"75c7f3c15af9df6e0f409e5f819157f72a20dc5b9c513343ef292d71795a810b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:20.301778Z","signature_b64":"Lzgpm6VJtcRxpDEwKy7Xsslaw2axgc8lw1YWyoOhyTozN4GX0xWw5CroETpQKZJ8igOqcZYMLmzJMxrxcqDMDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75c7f3c15af9df6e0f409e5f819157f72a20dc5b9c513343ef292d71795a810b","last_reissued_at":"2026-05-18T01:19:20.301277Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:20.301277Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.02300","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":"2026-05-18T01:19:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OzkrLMo7mwwtrRl0u6WJor5q5KWE0rC12e1cPVpMZorlB6AfiO/kUsGfST9+ow908jyD/+I+leFTvsOotDGnCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T21:11:56.009170Z"},"content_sha256":"4a7cc86d4cbbae5172e8aa171ffda5464e9fbf065dbc6c443e1288c7fdf80781","schema_version":"1.0","event_id":"sha256:4a7cc86d4cbbae5172e8aa171ffda5464e9fbf065dbc6c443e1288c7fdf80781"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:OXD7HQK27HPW4D2ATZPYDEKX64","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianxiong Xiao, Shuran Song","submitted_at":"2015-11-07T04:34:18Z","abstract_excerpt":"We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent. We introduce Deep Sliding Shapes, a 3D ConvNet formulation that takes a 3D volumetric scene from a RGB-D image as input and outputs 3D object bounding boxes. In our approach, we propose the first 3D Region Proposal Network (RPN) to learn objectness from geometric shapes and the first joint Object Recognition Network (ORN) to extract geometric features in 3D and color features in 2D. In particular, we handle objects of various sizes by train"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.02300","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":""},"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":"2026-05-18T01:19:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WbZ6tW1xaVk7KRXfkSS0GKUGH089S9g24/uoghJ+paENjKaznRhHmbZEWSpzjh354aoEP6n1eCE7ESlq3Uq6CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T21:11:56.009864Z"},"content_sha256":"fd4620d06dea48a98282569449914544cdf826f44ca0b609493894f0e3d73a77","schema_version":"1.0","event_id":"sha256:fd4620d06dea48a98282569449914544cdf826f44ca0b609493894f0e3d73a77"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OXD7HQK27HPW4D2ATZPYDEKX64/bundle.json","state_url":"https://pith.science/pith/OXD7HQK27HPW4D2ATZPYDEKX64/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OXD7HQK27HPW4D2ATZPYDEKX64/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":"2026-05-29T21:11:56Z","links":{"resolver":"https://pith.science/pith/OXD7HQK27HPW4D2ATZPYDEKX64","bundle":"https://pith.science/pith/OXD7HQK27HPW4D2ATZPYDEKX64/bundle.json","state":"https://pith.science/pith/OXD7HQK27HPW4D2ATZPYDEKX64/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OXD7HQK27HPW4D2ATZPYDEKX64/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:OXD7HQK27HPW4D2ATZPYDEKX64","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":"64f7d78d2f6ab67ddf921f2b6f1d243ff110f72e24c6e4eb2b0fbc03de1cf356","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-07T04:34:18Z","title_canon_sha256":"f44706395c1257cb113a0592b0d755b4c8d92c995c51f605b537b08cc2aba638"},"schema_version":"1.0","source":{"id":"1511.02300","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.02300","created_at":"2026-05-18T01:19:20Z"},{"alias_kind":"arxiv_version","alias_value":"1511.02300v2","created_at":"2026-05-18T01:19:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.02300","created_at":"2026-05-18T01:19:20Z"},{"alias_kind":"pith_short_12","alias_value":"OXD7HQK27HPW","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"OXD7HQK27HPW4D2A","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"OXD7HQK2","created_at":"2026-05-18T12:29:34Z"}],"graph_snapshots":[{"event_id":"sha256:fd4620d06dea48a98282569449914544cdf826f44ca0b609493894f0e3d73a77","target":"graph","created_at":"2026-05-18T01:19:20Z","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"},"paper":{"abstract_excerpt":"We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent. We introduce Deep Sliding Shapes, a 3D ConvNet formulation that takes a 3D volumetric scene from a RGB-D image as input and outputs 3D object bounding boxes. In our approach, we propose the first 3D Region Proposal Network (RPN) to learn objectness from geometric shapes and the first joint Object Recognition Network (ORN) to extract geometric features in 3D and color features in 2D. In particular, we handle objects of various sizes by train","authors_text":"Jianxiong Xiao, Shuran Song","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-07T04:34:18Z","title":"Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.02300","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":"sha256:4a7cc86d4cbbae5172e8aa171ffda5464e9fbf065dbc6c443e1288c7fdf80781","target":"record","created_at":"2026-05-18T01:19:20Z","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":"64f7d78d2f6ab67ddf921f2b6f1d243ff110f72e24c6e4eb2b0fbc03de1cf356","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-07T04:34:18Z","title_canon_sha256":"f44706395c1257cb113a0592b0d755b4c8d92c995c51f605b537b08cc2aba638"},"schema_version":"1.0","source":{"id":"1511.02300","kind":"arxiv","version":2}},"canonical_sha256":"75c7f3c15af9df6e0f409e5f819157f72a20dc5b9c513343ef292d71795a810b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75c7f3c15af9df6e0f409e5f819157f72a20dc5b9c513343ef292d71795a810b","first_computed_at":"2026-05-18T01:19:20.301277Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:19:20.301277Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Lzgpm6VJtcRxpDEwKy7Xsslaw2axgc8lw1YWyoOhyTozN4GX0xWw5CroETpQKZJ8igOqcZYMLmzJMxrxcqDMDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:19:20.301778Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.02300","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4a7cc86d4cbbae5172e8aa171ffda5464e9fbf065dbc6c443e1288c7fdf80781","sha256:fd4620d06dea48a98282569449914544cdf826f44ca0b609493894f0e3d73a77"],"state_sha256":"6f52ec1ebbf30d31ad93f83a233e86f207b236eb73d4839c45c624cf22ff7b29"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qji0ykCdBXQ09gdgDALFdkCEnvxeMbN/MVl1vQaHyXp+e62fTQBHvKio2aBJwUQ2tizvtERuBABzpPfm25a7DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T21:11:56.013485Z","bundle_sha256":"4b1468124c00ffc683da815a38f4b0b64541471c0dc50dccb135fb458f6e228d"}}