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"dangling_parent_count": 0, "cycle_node_count": 0, "valid": 0 } } }, "group_scores": { "pipeline_availability": 100.0, "geometry": 46.881688070368845, "part_decomposition": 100.0, "kinematics": 0.0, "motion_consistency": 0.0, "ot_routing_diagnostics": 0.0 }, "weighted_score": 38.98131916900561, "vlm_card": "/data/250010098/PAct-Transporter/reports/20260513_pact_transporter_exploration/runs/pact_eval100_easycase_probe_20260521/vlm_cards/025_GRScenes_large_furniture_large_furniture_grscenes_cabinet_5f26d9d3c405_a11992.png", "vlm": { "ok": true, "model": "gemini-2.5-flash", "text": "以下是对PAct在该样本上表现的简短测评和问题发现：\n\n1. **几何完整性与Part分解：** PAct成功地将物体分解为2个part，与GT parts数量一致（part_count_mae=0）。从2D mask看，分解是基于水平分割的，这与输入图像中物体下方的两条细线（可能代表抽屉或门缝）相符。\n2. **关节数量/类型与运动可行性：** GT movable为0，表明该物体在真实情况下是不可动的。然而，PAct的joint_f1为0.000，这可能意味着PAct未能识别出任何关节，或者错误地识别了关节。考虑到GT是不可动的，未能识别出关节是符合预期的。\n3. **对2D分割的依赖：** PAct的part分解与2D part mask高度一致，表明模型强烈依赖2D分割信息来定义部件。这种依赖性在处理不可动物体时可能不是问题，但对于复杂的可动物体，如果2D分割无法准确反映3D部件和关节，可能会导致生成的三维铰链物体不准确。\n4. **PActscore较低：** PAct weighted_score为38.98，相对较低，这可能反映了在其他未直接展示的指标（如几何精度、关节位置/轴线精度等）上存在不足，即使part数量匹配。", "raw": { "candidates": [ { "content": { "parts": [ { "text": "以下是对PAct在该样本上表现的简短测评和问题发现：\n\n1. **几何完整性与Part分解：** PAct成功地将物体分解为2个part，与GT parts数量一致（part_count_mae=0）。从2D mask看，分解是基于水平分割的，这与输入图像中物体下方的两条细线（可能代表抽屉或门缝）相符。\n2. **关节数量/类型与运动可行性：** GT movable为0，表明该物体在真实情况下是不可动的。然而，PAct的joint_f1为0.000，这可能意味着PAct未能识别出任何关节，或者错误地识别了关节。考虑到GT是不可动的，未能识别出关节是符合预期的。\n3. **对2D分割的依赖：** PAct的part分解与2D part mask高度一致，表明模型强烈依赖2D分割信息来定义部件。这种依赖性在处理不可动物体时可能不是问题，但对于复杂的可动物体，如果2D分割无法准确反映3D部件和关节，可能会导致生成的三维铰链物体不准确。\n4. **PActscore较低：** PAct weighted_score为38.98，相对较低，这可能反映了在其他未直接展示的指标（如几何精度、关节位置/轴线精度等）上存在不足，即使part数量匹配。" } ], "role": "model" }, "finishReason": "STOP", "index": 0 } ], "usageMetadata": { "promptTokenCount": 433, "candidatesTokenCount": 311, "totalTokenCount": 744, "promptTokensDetails": [ { "modality": 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"not_applicable", "note": "Aggregate-only metric; sample count is 5." }, "macro_score_by_problem_tag": { "value": null, "status": "not_applicable", "note": "Aggregate-only metric; sample count is 5." }, "worst_10pct_score": { "value": null, "status": "not_applicable", "note": "Aggregate-only metric; sample count is 5." }, "failure_cluster_count": { "value": null, "status": "not_provided", "note": "Requires larger batch clustering." } }, "debug": { "pred_part_count": 2, "gt_part_count": 2, "pred_joint_count": 1, "gt_joint_count": 1, "matches": [ { "type_mismatch": 0.0, "axis_error_deg": 90.0, "origin_error": 0.6198490669152915, "limit_error": 65.01169245249042, "accepted": false } ], "pred_tree": { "root_count": 1, "duplicate_ids": 0, "dangling_parent_count": 0, "cycle_node_count": 0, "valid": 1 }, "ref_tree": { "root_count": 1, "duplicate_ids": 0, "dangling_parent_count": 0, "cycle_node_count": 0, "valid": 1 } } }, "group_scores": { "pipeline_availability": 100.0, "geometry": 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null, "status": "not_applicable", "note": "Aggregate-only metric; see report.aggregate.by_source." }, "macro_score_by_category": { "value": null, "status": "not_applicable", "note": "Aggregate-only metric; sample count is 5." }, "macro_score_by_problem_tag": { "value": null, "status": "not_applicable", "note": "Aggregate-only metric; sample count is 5." }, "worst_10pct_score": { "value": null, "status": "not_applicable", "note": "Aggregate-only metric; sample count is 5." }, "failure_cluster_count": { "value": null, "status": "not_provided", "note": "Requires larger batch clustering." } }, "debug": { "pred_part_count": 2, "gt_part_count": 2, "pred_joint_count": 1, "gt_joint_count": 1, "matches": [ { "type_mismatch": 0.0, "axis_error_deg": 90.0, "origin_error": 0.5634717135442822, "limit_error": 60.68411190907029, "accepted": false } ], "pred_tree": { "root_count": 1, "duplicate_ids": 0, "dangling_parent_count": 0, "cycle_node_count": 0, "valid": 1 }, "ref_tree": { "root_count": 1, 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