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ABC Dataset

This experiment consists in classifying edge points on the chunk 0000 (7167 models) of the ABC Dataset. We randomly selected 200 and 50 models for training and validation, respectively:

  • Training models: 0037, 0039, 0044, 0048, 0101, 0116, 0124, 0137, 0143, 0150, 0156, 0189, 0202, 0230, 0233, 0254, 0299, 0310, 0324, 0342, 0374, 0382, 0387, 0392, 0435, 0457, 0467, 0480, 0501, 0502, 0513, 0533, 0542, 0550, 0551, 0553, 0584, 0603, 0618, 0640, 0646, 0654, 0662, 0664, 0673, 0683, 0686, 0688, 0746, 0753, 0794, 0840, 0871, 0901, 0915, 0925, 0950, 0959, 0980, 0982, 1001, 1022, 1035, 1043, 1054, 1058, 1080, 1095, 1139, 1142, 1143, 1154, 1173, 1193, 1197, 1203, 1226, 1231, 1242, 1250, 1263, 1299, 1301, 1314, 1341, 1361, 1369, 1374, 1387, 1390, 1393, 1401, 1403, 1405, 1413, 1418, 1452, 1473, 1521, 1532, 1536, 1538, 1559, 1589, 1599, 1607, 1608, 1611, 1613, 1617, 1627, 1634, 1675, 1697, 1700, 1713, 1722, 1724, 1729, 1739, 1744, 1751, 1761, 1811, 1840, 1841, 1848, 1877, 1896, 1898, 1903, 1913, 1944, 1950, 1954, 1955, 1961, 1967, 2034, 2036, 2046, 2082, 2089, 2107, 2118, 2131, 2135, 2138, 2161, 2170, 2177, 2189, 2201, 2207, 2236, 2245, 2295, 2304, 2310, 2329, 2340, 2354, 2358, 2389, 2425, 2426, 2427, 2430, 2432, 2435, 2438, 2452, 2472, 2487, 2492, 2499, 2517, 2520, 2538, 2540, 2584, 2586, 2596, 2609, 2678, 2685, 2686, 2690, 2714, 2719, 2726, 2732, 2744, 2763, 2768, 2771, 2776, 2777, 2799, 2800,
  • Validation models: 0008, 0091, 0138, 0198, 0277, 0353, 0402, 0487, 0541, 0576, 0643, 0667, 0713, 0844, 0939, 0994, 1051, 1106, 1155, 1217, 1258, 1323, 1384, 1402, 1433, 1534, 1594, 1612, 1672, 1716, 1743, 1813, 1884, 1937, 1957, 2038, 2117, 2139, 2192, 2248, 2337, 2392, 2431, 2465, 2507, 2572, 2667, 2703, 2738, 2775.

Examples of 3d models

Model description

Select a classification in the list:

Select a model in the list:

Legend

  • Flat
  • Sharp
  • Smooth

Interactive WebGL viewer

(click, drag and scroll to interact with the 3D model)

Statistical analysis

Indicators: median value per method

Click on a column header to change sorting.

Indicators distributions per method

This plot shows, for each method and training set, the distributions of several indicators for the whole dataset. Use the drop-down menue below to change the indicator (both the description and the plot should be updated). Note that the plot is interactive: change zoom, range and access method full name using mouse interaction.
Current indicator:

Bad (indicator 25% smallest values) and good (indicator 10% higher values) areas are highlighted in the plot in red and green, respectively. Efficient methods should have few samples in the red area (the lower, the better), while having most of its samples in the green area (the higher, the better).

Precision/Recall

This plot shows a representative score per approach: each method is shown as point sample, such that (select mean or median below):

  • Its position is the mean precision/recall score for the whole dataset,
  • Its position is the median precision/recall score for the whole dataset.

The plot is interactive: change zoom, range and access method full name using mouse interaction.

Visualization type:

Precision/Recall distributions - Methods comparisons

These graphs show the precision/recall values as scatter plot (each 3D model is a sample) and its associated density function for the whole dataset.

Left dataset:
Right dataset: