{"id":"18e76bec-c92c-4a24-b5a9-1f9afb7a46a9","arxiv_id":"1908.00382","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A cascaded context pyramid network with guided residual refinement achieves state-of-the-art full-resolution 3D semantic scene completion from a single depth map on SUNCG and NYU datasets.","lead":"This paper introduces CCPNet, a neural network that predicts the full 3D layout and object labels of an indoor scene from a single depth image. It reports higher accuracy and lower computational cost than prior methods on standard 3D scene completion benchmarks.","discovery_kind":"new_method","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-14T15:59:09.448268+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}