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ProbeSDF_Result

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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
candidate_id: string
batch: string
slug: string
chinese_name: string
source_path: string
note: string
decision: string
availability: string
mesh: struct<sha256: string, bytes: int64, geometry_count: int64, vertex_count: int64, face_count: int64, extents_m: list<item: double>, visual_kinds: list<item: string>, median_vertex_rgb: list<item: int64>>
reason: string
replacement: string
vs
schema: string
dataset: string
object_count: int64
units: string
objects: list<item: struct<schema: string, candidate_id: string, batch: string, slug: string, name_zh: string, unit: string, appearance: string, canonical_coordinate_frame: string, canonical_center_definition: string, source: struct<original_path: string, file: string, sha256: string>, canonical_mesh: struct<file: string, sha256: string, removed_source_aabb_midpoint_m: list<item: double>, vertex_count: int64, face_count: int64, extents_m: list<item: double>>, historical_note: string, catalog_decision: string, directory: string, preview: string, observed_mesh: struct<file: string, removed_source_aabb_midpoint_m: list<item: double>, vertex_count: int64, face_count: int64, extents_m: list<item: double>>, prior_visual_review: struct<state: string, name_zh: string, reason: string>, quality: struct<watertight: bool, collision_ready: bool, metric_accuracy_validated: bool, self_intersections_certified: bool, notes_zh: string, visual_review: string, notes: string>, closed_variant: struct<directory: string, report: string, original_files_preserved: bool>, provenance: struct<source_run: string, calibration: string, camera_parameters: string, scene: string, reconstruction: string, centering: string, source_frame_map: string>, reconstruction: struct<method: string, native_size_wh: list<item: int64>, views: int64, voxel_mm: double, elapsed_train_and_center_s: double>>>
excluded_entries: list<item: struct<candidate_id: string, batch: string, slug: string, chinese_name: string, source_path: string, note: string, decision: string, availability: string, mesh: struct<sha256: string, bytes: int64, geometry_count: int64, vertex_count: int64, face_count: int64, extents_m: list<item: double>, visual_kinds: list<item: string>, median_vertex_rgb: list<item: int64>>, reason: string, replacement: string>>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 564, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5040, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              candidate_id: string
              batch: string
              slug: string
              chinese_name: string
              source_path: string
              note: string
              decision: string
              availability: string
              mesh: struct<sha256: string, bytes: int64, geometry_count: int64, vertex_count: int64, face_count: int64, extents_m: list<item: double>, visual_kinds: list<item: string>, median_vertex_rgb: list<item: int64>>
              reason: string
              replacement: string
              vs
              schema: string
              dataset: string
              object_count: int64
              units: string
              objects: list<item: struct<schema: string, candidate_id: string, batch: string, slug: string, name_zh: string, unit: string, appearance: string, canonical_coordinate_frame: string, canonical_center_definition: string, source: struct<original_path: string, file: string, sha256: string>, canonical_mesh: struct<file: string, sha256: string, removed_source_aabb_midpoint_m: list<item: double>, vertex_count: int64, face_count: int64, extents_m: list<item: double>>, historical_note: string, catalog_decision: string, directory: string, preview: string, observed_mesh: struct<file: string, removed_source_aabb_midpoint_m: list<item: double>, vertex_count: int64, face_count: int64, extents_m: list<item: double>>, prior_visual_review: struct<state: string, name_zh: string, reason: string>, quality: struct<watertight: bool, collision_ready: bool, metric_accuracy_validated: bool, self_intersections_certified: bool, notes_zh: string, visual_review: string, notes: string>, closed_variant: struct<directory: string, report: string, original_files_preserved: bool>, provenance: struct<source_run: string, calibration: string, camera_parameters: string, scene: string, reconstruction: string, centering: string, source_frame_map: string>, reconstruction: struct<method: string, native_size_wh: list<item: int64>, views: int64, voxel_mm: double, elapsed_train_and_center_s: double>>>
              excluded_entries: list<item: struct<candidate_id: string, batch: string, slug: string, chinese_name: string, source_path: string, note: string, decision: string, availability: string, mesh: struct<sha256: string, bytes: int64, geometry_count: int64, vertex_count: int64, face_count: int64, extents_m: list<item: double>, visual_kinds: list<item: string>, median_vertex_rgb: list<item: int64>>, reason: string, replacement: string>>

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

ProbeSDF Result

LMCAP 历史物体重建资产集，共 42 个已确认候选。单位统一为米，颜色为 mesh 中保存的逐顶点 RGB。

每个物体目录包含：

  • mesh_source.*：历史原始 mesh，不改几何与坐标。
  • mesh_centered.ply：以 AABB 中点居中的统一 PLY。
  • preview.jpg：正面、背面、侧面真实顶点色预览。
  • metadata.json：名称、尺寸、哈希、来源与居中变换。

Objects

ID 中文名称 Slug 批次 目录
H001 浅灰色 marker 盒 light_gray_marker_box 0707 objects/h001_light_gray_marker_box
H010 黑色产品盒 black_product_box 0807 objects/h010_black_product_box
H011 黑色长方盒 black_rectangular_box 0807 objects/h011_black_rectangular_box
H012 棕色纸盒 brown_cardboard_box 0807 objects/h012_brown_cardboard_box
H013 奶龙 yellow_orange_milk_dragon 0807 objects/h013_yellow_orange_milk_dragon
H021 黑色键盘 black_keyboard 0811 objects/h021_black_keyboard
H022 黑色鼠标 black_mouse 0811 objects/h022_black_mouse
H023 黑色圆形设备 black_round_device 0811 objects/h023_black_round_device
H024 白色笔杆红色笔帽马克笔 white_marker_red_cap 0811 objects/h024_white_marker_red_cap
H025 黑色水瓶 black_water_bottle 0811 objects/h025_black_water_bottle
H026 黑色触控笔 black_stylus_pen 0811 objects/h026_black_stylus_pen
H027 黑色长方形电子设备 black_rectangular_device 0811 objects/h027_black_rectangular_device
H030 银色金属罐 silver_metal_can 0813 objects/h030_silver_metal_can
H031 蓝色纸盒 blue_paper_box 0813 objects/h031_blue_paper_box
H032 银色金属杯 silver_metal_cup 0813 objects/h032_silver_metal_cup
H040 白色收纳盒 white_storage_case 0817 objects/h040_white_storage_case
H041 红色饮料罐 red_beverage_can 0818 objects/h041_red_beverage_can
H050 黑色地瓜派盒子 black_sweet_potato_pie_box 0819 objects/h050_black_sweet_potato_pie_box
H051 黑色杯子 black_cup 0819 objects/h051_black_cup
H052 黄色上开口空心盒 yellow_open_hollow_box 0819 objects/h052_yellow_open_hollow_box
H100 棕色开口盒 brown_open_box 0825 objects/h100_brown_open_box
H101 黑色平底锅 black_frying_pan 0825 objects/h101_black_frying_pan
H103 棕色木铲 brown_wooden_spatula 0825 objects/h103_brown_wooden_spatula
H104 粉色计算器 pink_calculator 0825 objects/h104_pink_calculator
H105 带 marker 点的大碗 large_bowl_with_markers 0825 objects/h105_large_bowl_with_markers
H106 带 marker 点的小碗 small_bowl_with_markers 0825 objects/h106_small_bowl_with_markers
H107 带 marker 点的盘子 plate_with_markers 0825 objects/h107_plate_with_markers
H108 黑色长方盒 black_rectangular_box 0825 objects/h108_black_rectangular_box
H109 黄色开口盒 yellow_open_box 0825 objects/h109_yellow_open_box
H110 无 marker 点的粉色哑铃 pink_dumbbell_without_markers 0825 objects/h110_pink_dumbbell_without_markers
H111 蓝色百事可乐罐 blue_pepsi_can 0825 objects/h111_blue_pepsi_can
H112 半透明收纳盒 translucent_storage_box 0825 objects/h112_translucent_storage_box
H200 黑色正面白色背面手机 black_front_white_back_smartphone 0828 objects/h200_black_front_white_back_smartphone
H201 带 marker 点的酒瓶 black_white_wine_bottle_with_markers 0828 objects/h201_black_white_wine_bottle_with_markers
H202 带 marker 点的粉色哑铃 pink_dumbbell_with_markers 0828 objects/h202_pink_dumbbell_with_markers
H203 薄荷绿浇水壶 mint_green_watering_can 0828 objects/h203_mint_green_watering_can
H204 带 marker 点的透明黑把手壶 transparent_kettle_black_handle_with_markers 0828 objects/h204_transparent_kettle_black_handle_with_markers
H205 粉色碗 pink_bowl 0828 objects/h205_pink_bowl
H206 带 marker 点的绿色苹果 green_apple_with_markers 0828 objects/h206_green_apple_with_markers
H207 带 marker 的蓝色杯子 blue_cup_with_markers 0828 objects/h207_blue_cup_with_markers
H208 蓝色开口容器 blue_open_container 0828 objects/h208_blue_open_container
H209 蓝色杯子 blue_cup 0828 objects/h209_blue_cup

manifest.json 是机器可读总索引；excluded_entries.json 记录经人工确认排除的重复候选。

0910 新增可见表面重建资产

本次检查17件导出网格，发布3份可见表面资产，14份因缺面、几何畸变或部件缺失暂缓。 这些是原始ProbeSDF网格，不是已封闭的碰撞模型；不保证未拍摄底面，也未经独立米制精度评测。 几何仅平移到AABB中心，没有修改旋转或尺度。源坐标变换见各物体center_report.json。 完整检查清单与图片

物体 实际PLY预览 限制
遥控器 预览 主轮廓、按键面及可见侧面保留；纹理略模糊，背面未完整观测，非封闭资产。
红色消防自救呼吸器盒 预览 盒体主要轮廓、顶盖和标签保留；局部纹理有畸变和表面起伏，底面不作为可信观测。
带标记点木铲 预览 主要轮廓、柄和标记点保留；背面未观测，局部边缘及标记点有误差，非封闭资产。

当前总索引包含45份资产：历史42份和本次3份。历史条目及原文件保持不变。

0910 主动封闭更新

遥控器、红盒、木铲已增加 closed_v1/。各metadata和总索引的canonical_mesh现指向封闭版本；旧原始网格保留。 新增底面为推测几何，尺寸变化小于0.001 mm，坐标变换见各closed_v1/report.json。watertight与正体积检查通过，不代表完整碰撞或精度认证。

0910 全量补面版本

17份物体补面版本已按用户要求统一发布，见各目录 closed_v1/。canonical_mesh 指向补面版本，原始观测网格保留于 observed_mesh。 发布状态为 user_accepted_completion，不是全部形状检查通过。补面包含推测几何，部分物体仍有形状缺失或截平；逐件原检查记录与补面假设见 metadata.json 和 closed_v1/report.json。拓扑 watertight 不等于无孔洞外观、完整实物形状或碰撞可用。

0914-br 环绕重建

新增三份原始可见表面重建，总索引现为 62 份。历史产物保留。三份均非封闭网格，不标为碰撞可用；尤其白碗内腔偏浅，不能作为准确空心碗。

  • 白色黑边碗：实际网格预览。碗口及外轮廓已恢复，但内腔明显偏浅，外壁存在几何起伏及黑色纹理伪影。不可作为准确的空心碗或碰撞模型。
  • 黄色杨桃模型：实际网格预览。黄色杨桃模型的主要棱线和可见轮廓已恢复；侧面和接触底面存在黑色纹理、表面起伏及缺面，底面形状不可信。
  • 黑色手机：实际网格预览。手机正面和可见边缘已恢复；反光表面纹理不均，背面未被直接观测，不代表完整双面手机几何。
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