ZiyuG commited on
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Update sam2point/configs.py

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  1. sam2point/configs.py +71 -34
sam2point/configs.py CHANGED
@@ -10,60 +10,74 @@ sample_2 = {'path': 'data/S3DIS/Area_1_conferenceRoom_1.txt',
10
 
11
  sample_3 = {'path': 'data/S3DIS/Area_2_WC_1.txt',
12
  'point_prompts': [[0.31414868, 0.59265659, 0.50951199], [0.6628697, 0.90842333, 0.34036394],[0.63868905, 0.36414687, 0.94954508],
13
- [0.11171063, 0.85788337, 0.18072787],
14
  [0.88589129, 0.59049676, 0.44830438],],
15
  'box_prompts': [[0.35, 0.8, 0.05, 0.45, 1.0, 0.4], [0.48, 0.65, 0.0, 0.55, 0.99, 0.99], [0.57, 0.2, 0.85, 0.7, 0.48, 1.0],
16
- [0.61, 0., 0.33, 0.71, 0.13, 0.51],],
17
  'mask_prompts': [[0.31414868, 0.59265659, 0.50951199], [0.6628697, 0.90842333, 0.34036394],[0.63868905, 0.36414687, 0.94954508],
18
- [0.11171063, 0.85788337, 0.18072787],
19
  [0.88589129, 0.59049676, 0.44830438],],
20
  }
21
 
22
 
23
  sample_4 = {'path': 'data/S3DIS/Area_4_lobby_2.txt',
24
- 'point_prompts': [[0.19949431, 0.28597082, 0.25131625],
25
  [0.72566372, 0.3617284, 0.65601966], [0.50316056, 0.57519641, 0.32186732],
26
  [0.46396966, 0.52345679, 0.54756055],],
27
  'box_prompts': [[0.42, 0.45, 0.3, 0.49, 0.54, 0.65], [0.45, 0.57, 0.27, 0.55, 0.63, 0.36], [0.17, 0.35, 0., 0.25, 0.4, 0.3],
28
  [0.15, 0.25, 0.4, 0.19, 0.33, 0.62], [0.17, 0.78, 0.27, 0.2, 0.84, 0.43]],
29
- 'mask_prompts': [[0.72566372, 0.3617284, 0.65601966], [0.50316056, 0.57519641, 0.32186732],
 
30
  [0.46396966, 0.52345679, 0.54756055],],
31
  }
32
 
33
  sample_1 = {'path': 'data/S3DIS/Area_5_office_3.txt',
34
- 'point_prompts': [
35
  [0.90161319, 0.51668286, 0.21546617], [0.98404538, 0.29024943, 0.51013408],
36
  [0.76369438, 0.32458698, 0.23542251]],
37
  'box_prompts': [[0., 0.48, 0.23, 0.12, 0.61, 0.31], [0.4, 0.25, 0., 0.6, 0.6, 0.3], [0.45, 0.85, 0.45, 0.65, 0.99, 0.55],
38
  [0.38, 0.95, 0.25, 0.48, 1.00, 0.42], [0.65, 0.45, 0., 0.75, 0.6, 0.3]],
39
  'mask_prompts': [[0.45080659, 0.88824101, 0.22856252],
40
  [0.90161319, 0.51668286, 0.21546617], [0.98404538, 0.29024943, 0.51013408],
41
- [0.76369438, 0.32458698, 0.23542251]],
42
  }
43
 
44
  sample_0 = {'path': 'data/S3DIS/Area_6_office_9.txt',
45
  'point_prompts': [[0.16548, 0.27853667, 0.1886402], [0.46150787, 0.09795895, 0.26989673], [0.2904479, 0.5073498, 0.28115318],
46
  [0.9304859, 0.40291342, 0.32013769], [0.802557, 0.5818576, 0.19074],
47
  [0.52659518, 0.5240772, 0.40165232], [0.29337714, 0.8905976, 0.2722375], [0.563984, 0.925, 0.3803788],],
 
48
  'box_prompts': [[0.1, 0.2, 0.0, 0.2, 0.3, 0.4], [0.1, 0.02, 0.2, 0.9, 0.2, 0.3], [0.7, 0.5, 0., 0.9, 0.7, 0.4],
49
  [0.85, 0.3, 0.02, 0.98, 0.5, 0.8], [0.4, 0.4, 0.3, 0.6, 0.6, 0.5], ],
50
  'mask_prompts': [[0.16548, 0.27853667, 0.1886402], [0.46150787, 0.09795895, 0.26989673], [0.2904479, 0.5073498, 0.28115318],
51
  [0.9304859, 0.40291342, 0.32013769], [0.802557, 0.5818576, 0.19074],
52
  [0.52659518, 0.5240772, 0.40165232], [0.29337714, 0.8905976, 0.2722375], [0.563984, 0.925, 0.3803788],]
 
53
  }
54
 
55
 
56
  S3DIS_samples = [sample_2, sample_3, sample_4, sample_1, sample_0]
57
 
58
 
59
- sample_1 = {'path': 'data/ScanNet/scene0005_01.pth',
60
- 'point_prompts': [[0.50845712, 0.4027696, 0.19570725], [0.26778319, 0.9830749, 0.44313431]],
61
- 'box_prompts': [[0.6, 0.6, 0., 0.83, 0.9, 0.33], [0.0, 0.57, 0.05, 0.15, 0.67, 0.48],
 
 
 
 
 
 
 
 
 
 
 
62
  [0.48, 0.95, 0.58, 0.8, 0.99, 0.9]],
63
  'mask_prompts': [[0.50845712, 0.4027696, 0.19570725], [0.26778319, 0.9830749, 0.44313431]],
64
  }
65
  sample_2 = {'path': 'data/ScanNet/scene0010_01.pth',
66
- 'point_prompts': [[0.86644632, 0.26297486, 0.5173167]],
67
  'box_prompts': [[0.6, 0.72, 0.0, 0.75, 0.85, 0.6], [0.75, 0.70, 0.5, 0.92, 0.92, 0.75], [0.05, 0.92, 0.05, 0.27, 1.0, 0.82],
68
  [0.35, 0.03, 0.15, 0.5, 0.1, 0.42], ],
69
  'mask_prompts': [[0.86644632, 0.26297486, 0.5173167]],
@@ -72,14 +86,18 @@ sample_2 = {'path': 'data/ScanNet/scene0010_01.pth',
72
 
73
  sample_3 = {'path': 'data/ScanNet/scene0016_02.pth',
74
  'point_prompts': [[0.2898192, 0.5845358, 0.7862434], [0.8251329,0.1763976,0.2942619]],
75
- 'box_prompts': [[0.72, 0.36, 0.1, 0.9, 0.75, 0.75], [0.27, 0.54, 0.7, 0.3, 0.65, 0.9],],
 
 
76
  'mask_prompts': [[0.2898192, 0.5845358, 0.7862434]],
77
  }
78
 
79
 
80
  sample_4 = {'path': 'data/ScanNet/scene0019_01.pth',
81
- 'point_prompts': [[0.52182293, 0.69650459, 0.36580974], [0.6603151, 0.26341686, 0.33537653],[0.03188787, 0.65648252, 0.43863711]],
82
- 'box_prompts': [[0.55, 0.22, 0.05, 0.72, 0.3, 0.58], [0.0, 0.27, 0.05, 0.2, 0.35, 0.45]],
 
 
83
  'mask_prompts': [[0.52182293, 0.69650459, 0.36580974], [0.6603151, 0.26341686, 0.33537653], [0.17163187, 0.30585486, 0.31457961], [0.03188787, 0.65648252, 0.43863711]],
84
  }
85
 
@@ -94,33 +112,34 @@ sample_6 = {'path': 'data/ScanNet/scene0002_00.pth',
94
  'mask_prompts': [[0.56711978, 0.74271345, 0.1753805 ], [0.61877084, 0.47617316, 0.23380645]],
95
  }
96
 
97
- ScanNet_samples = [sample_1, sample_2, sample_3, sample_4, sample_5, sample_6]
98
 
99
 
100
  sample_0 = {'path': 'data/Objaverse/plant.npy',
101
- 'point_prompts': [[0.50455284, 0.47794762, 0.0007253083], [0.28331658, 0.19435011, 0.77393067]],
102
  'voxel_size': [0.038, 0.04],
103
- 'box_prompts': [[0.08, 0.18, -0.02, 0.68, 0.73, 0.315]],
104
- 'voxel_size_box': [0.04, 0.05],
105
- 'mask_prompts': [[0.50455284, 0.47794762, 0.0007253083]],
 
106
  'voxel_size_mask': [0.038]
107
  }
108
 
109
 
110
  sample_1 = {'path': 'data/Objaverse/human.npy',
111
- 'point_prompts': [[0.57825595, 0.5005686, 0.11494722], [0.7136412, 0.49501216, 0.5020814 ], [0.7136412, 0.49501216, 0.5020814 ]],
112
  'voxel_size': [0.055, 0.045, 0.05],
113
  'box_prompts': [[0., 0.17, -0.01, 0.72, 0.80, 0.3], [-0.01, 0., 0.28, 0.8, 1, 0.82], [-0.01, 0.28, 0.89, 1, 0.72, 1.02]],
114
  'voxel_size_box': [0.055, 0.045, 0.055],
115
- 'mask_prompts': [[0.57825595, 0.5005686, 0.11494722], [0.7136412, 0.49501216, 0.5020814 ]],
116
  'voxel_size_mask': [0.055, 0.055],
117
  }
118
  sample_2 = {'path': 'data/Objaverse/lock.npy',
119
- 'point_prompts': [[0.6513301, 0.6753892, 0.52316076], [0.21359734, 0.6097132 , 0.7939796 ], [0.44947368, 0.21654338, 0.58450174]],
120
- 'voxel_size': [0.04, 0.05, 0.05],
121
- 'box_prompts': [[0.61, 0.4, 0.35, 0.8, 0.8, 0.6], [0.42, -0.02, -0.02, 1.02, 0.4, 1]],
122
- 'voxel_size_box': [0.04, 0.011],
123
- 'mask_prompts': [[0.6513301, 0.6753892, 0.52316076], [0.21359734, 0.6097132 , 0.7939796 ], [0.9157764, 0.1995991, 0.14024617]],
124
  'voxel_size_mask': [0.04, 0.055, 0.04],
125
  }
126
 
@@ -152,11 +171,11 @@ sample_5 = {'path': 'data/Objaverse/skateboard.npy',
152
  }
153
 
154
  sample_6 = {'path': 'data/Objaverse/popcorn_machine.npy',
155
- 'point_prompts': [[0.278306, 0.4913014, 0.7318756], [0.5867118, 0.1180351, 0.5844101]],
156
  'voxel_size': [0.04, 0.04],
157
  'box_prompts': [[0.208, 0.157, 0.493, 0.779, 0.89, 0.925]],
158
  'voxel_size_box': [0.04],
159
- 'mask_prompts': [[0.278306, 0.4913014, 0.7318756], [0.5867118, 0.1180351, 0.5844101]],
160
  'voxel_size_mask': [0.04, 0.04],
161
  }
162
 
@@ -182,7 +201,7 @@ sample_8 = {'path': 'data/Objaverse/bus_shelter.npy',
182
  sample_9 = {'path': 'data/Objaverse/thor_hammer.npy',
183
  'point_prompts': [[0.6211515, 0.5109989, 0.3867725], [0.44443, 0.2363458, 0.7229376]],
184
  'voxel_size': [0.05, 0.05, 0.05],
185
- 'box_prompts': [[0,0,0.723,1,1,1]],
186
  'voxel_size_box': [0.05, 0.05],
187
  'mask_prompts': [[0.44443, 0.2363458, 0.7229376]],
188
  'voxel_size_mask': [0.05],
@@ -191,7 +210,7 @@ sample_9 = {'path': 'data/Objaverse/thor_hammer.npy',
191
  sample_10 = {'path': 'data/Objaverse/horse.npy',
192
  'point_prompts': [[0.3359364, 0.7555879, 0.6848574], [0.9221735, 0.1779197, 0.1927067]],
193
  'voxel_size': [0.04, 0.04],
194
- 'box_prompts': [[0.65,0,0.3,1,1,0.79], [0.37, 0, 0, 1, 1, 0.2]],
195
  'voxel_size_box': [0.04, 0.04],
196
  'mask_prompts': [[0.3359364, 0.7555879, 0.6848574], [0.9221735, 0.1779197, 0.1927067]],
197
  'voxel_size_mask': [0.04, 0.04],
@@ -202,13 +221,28 @@ sample_11 = {'path': 'data/Objaverse/dinner_booth.npy',
202
  [0.9192697, 0.4469184, 0.0017635],
203
  [0.4987888, 0.6916906, 0.5106028]],
204
  'voxel_size': [0.04, 0.04],
205
- 'box_prompts': [[0.65,0,0.3,1,1,0.79], [0.37, 0, 0, 1, 1, 0.2]],
206
  'voxel_size_box': [0.04, 0.04],
207
  'mask_prompts': [[0.3359364, 0.7555879, 0.6848574], [0.9221735, 0.1779197, 0.1927067]],
208
  'voxel_size_mask': [0.04, 0.04],
209
  }
 
 
 
 
 
 
 
 
 
 
 
 
 
210
 
211
  Objaverse_samples = [sample_0, sample_1, sample_2, sample_3, sample_4, sample_5, sample_6, sample_7, sample_8, sample_9, sample_10, sample_11]
 
 
212
 
213
 
214
  sample_0 = {'path': 'data/KITTI/scene1.npy',
@@ -317,8 +351,8 @@ sample_4 = {'path': 'data/Semantic3D/patch1.npy',
317
  'voxel_size': [0.017, 0.017, 0.017, 0.017],
318
  'box_prompts': [],
319
  'voxel_size_box': [],
320
- 'mask_prompts': [[0.1857393, 0.2675134, 0.2463012]],
321
- 'voxel_size_mask': [0.01],
322
  }
323
 
324
  sample_5 = {'path': 'data/Semantic3D/patch50.npy',
@@ -343,4 +377,7 @@ sample_6 = {'path': 'data/Semantic3D/patch62.npy',
343
  Semantic3D_samples = [sample_0, sample_1, sample_2, sample_3, sample_4, sample_5, sample_6]
344
 
345
 
346
- VOXEL = {"point": "voxel_size", "box": "voxel_size_box", "mask": "voxel_size_mask"}
 
 
 
 
10
 
11
  sample_3 = {'path': 'data/S3DIS/Area_2_WC_1.txt',
12
  'point_prompts': [[0.31414868, 0.59265659, 0.50951199], [0.6628697, 0.90842333, 0.34036394],[0.63868905, 0.36414687, 0.94954508],
13
+ [0.11171063, 0.85788337, 0.18072787], #[0.76159073, 0.82289417, 0.68899917],
14
  [0.88589129, 0.59049676, 0.44830438],],
15
  'box_prompts': [[0.35, 0.8, 0.05, 0.45, 1.0, 0.4], [0.48, 0.65, 0.0, 0.55, 0.99, 0.99], [0.57, 0.2, 0.85, 0.7, 0.48, 1.0],
16
+ [0.61, 0., 0.33, 0.71, 0.13, 0.51],], # [0.51, 0., 0., 0.61, 0.15, 0.37],
17
  'mask_prompts': [[0.31414868, 0.59265659, 0.50951199], [0.6628697, 0.90842333, 0.34036394],[0.63868905, 0.36414687, 0.94954508],
18
+ [0.11171063, 0.85788337, 0.18072787], #[0.76159073, 0.82289417, 0.68899917],
19
  [0.88589129, 0.59049676, 0.44830438],],
20
  }
21
 
22
 
23
  sample_4 = {'path': 'data/S3DIS/Area_4_lobby_2.txt',
24
+ 'point_prompts': [[0.19949431, 0.28597082, 0.25131625], #[0.30316056, 0.87452301, 0.33696034],
25
  [0.72566372, 0.3617284, 0.65601966], [0.50316056, 0.57519641, 0.32186732],
26
  [0.46396966, 0.52345679, 0.54756055],],
27
  'box_prompts': [[0.42, 0.45, 0.3, 0.49, 0.54, 0.65], [0.45, 0.57, 0.27, 0.55, 0.63, 0.36], [0.17, 0.35, 0., 0.25, 0.4, 0.3],
28
  [0.15, 0.25, 0.4, 0.19, 0.33, 0.62], [0.17, 0.78, 0.27, 0.2, 0.84, 0.43]],
29
+ 'mask_prompts': [#[0.19949431, 0.28597082, 0.25131625], [0.30316056, 0.87452301, 0.33696034],
30
+ [0.72566372, 0.3617284, 0.65601966], [0.50316056, 0.57519641, 0.32186732],
31
  [0.46396966, 0.52345679, 0.54756055],],
32
  }
33
 
34
  sample_1 = {'path': 'data/S3DIS/Area_5_office_3.txt',
35
+ 'point_prompts': [ #[0.45080659, 0.88824101, 0.22856252], [0.55965254, 0.72432783, 0.00623636], [0.36589257, 0.93683188, 0.64826941],
36
  [0.90161319, 0.51668286, 0.21546617], [0.98404538, 0.29024943, 0.51013408],
37
  [0.76369438, 0.32458698, 0.23542251]],
38
  'box_prompts': [[0., 0.48, 0.23, 0.12, 0.61, 0.31], [0.4, 0.25, 0., 0.6, 0.6, 0.3], [0.45, 0.85, 0.45, 0.65, 0.99, 0.55],
39
  [0.38, 0.95, 0.25, 0.48, 1.00, 0.42], [0.65, 0.45, 0., 0.75, 0.6, 0.3]],
40
  'mask_prompts': [[0.45080659, 0.88824101, 0.22856252],
41
  [0.90161319, 0.51668286, 0.21546617], [0.98404538, 0.29024943, 0.51013408],
42
+ [0.76369438, 0.32458698, 0.23542251]], #[0.55965254, 0.72432783, 0.00623636], [0.36589257, 0.93683188, 0.64826941],
43
  }
44
 
45
  sample_0 = {'path': 'data/S3DIS/Area_6_office_9.txt',
46
  'point_prompts': [[0.16548, 0.27853667, 0.1886402], [0.46150787, 0.09795895, 0.26989673], [0.2904479, 0.5073498, 0.28115318],
47
  [0.9304859, 0.40291342, 0.32013769], [0.802557, 0.5818576, 0.19074],
48
  [0.52659518, 0.5240772, 0.40165232], [0.29337714, 0.8905976, 0.2722375], [0.563984, 0.925, 0.3803788],],
49
+ # [0.73819816, 0.913756, 0.2815835 ], [0.338812, 0.48102965, 0.34078142]],
50
  'box_prompts': [[0.1, 0.2, 0.0, 0.2, 0.3, 0.4], [0.1, 0.02, 0.2, 0.9, 0.2, 0.3], [0.7, 0.5, 0., 0.9, 0.7, 0.4],
51
  [0.85, 0.3, 0.02, 0.98, 0.5, 0.8], [0.4, 0.4, 0.3, 0.6, 0.6, 0.5], ],
52
  'mask_prompts': [[0.16548, 0.27853667, 0.1886402], [0.46150787, 0.09795895, 0.26989673], [0.2904479, 0.5073498, 0.28115318],
53
  [0.9304859, 0.40291342, 0.32013769], [0.802557, 0.5818576, 0.19074],
54
  [0.52659518, 0.5240772, 0.40165232], [0.29337714, 0.8905976, 0.2722375], [0.563984, 0.925, 0.3803788],]
55
+ # [0.73819816, 0.913756, 0.2815835 ], [0.338812, 0.48102965, 0.34078142]],
56
  }
57
 
58
 
59
  S3DIS_samples = [sample_2, sample_3, sample_4, sample_1, sample_0]
60
 
61
 
62
+
63
+
64
+ # sample_0 = {'path': 'data/ScanNet/scene0001_01.pth',
65
+ # 'point_prompts': [[0.48574361, 0.70011979, 0.21237852],
66
+ # [0.28947121, 0.15144145, 0.24688229], [0.3489365, 0.53977334, 0.02221746],
67
+ # [0.48059669, 0.88824904, 0.25690538]], #[0.48760539, 0.12294616, 0.25476629], #[0.48738128, 0.63986588, 0.25412986],
68
+ # 'box_prompts': [[0.25, 0.63, 0., 0.57, 0.75, 0.37], [0.42, 0.83, 0., 0.54, 0.94, 0.3], [0.4, 0.05, 0.0, 0.53, 0.2, 0.3],
69
+ # [0.12, 0.35, 0.0, 0.22, 0.45, 0.24], [0.88, 0.2, 0.1, 0.95, 0.8, 0.48]],
70
+ # }
71
+
72
+
73
+ sample_1 = {'path': 'data/ScanNet/scene0005_01.pth', #[0.04293748, 0.38949549, 0.314679], [0.24069363, 0.51310396, 0.01414406],
74
+ 'point_prompts': [[0.50845712, 0.4027696, 0.19570725], [0.26778319, 0.9830749, 0.44313431]], #[0.6458742, 0.33051795, 0.31433141], [0.11679079, 0.60943264, 0.40539789],
75
+ 'box_prompts': [[0.6, 0.6, 0., 0.83, 0.9, 0.33], [0.0, 0.57, 0.05, 0.15, 0.67, 0.48], #[0.41, 0.65, 0., 0.56, 0.77, 0.35],
76
  [0.48, 0.95, 0.58, 0.8, 0.99, 0.9]],
77
  'mask_prompts': [[0.50845712, 0.4027696, 0.19570725], [0.26778319, 0.9830749, 0.44313431]],
78
  }
79
  sample_2 = {'path': 'data/ScanNet/scene0010_01.pth',
80
+ 'point_prompts': [[0.86644632, 0.26297486, 0.5173167]], #[0.15311202, 0.44485098, 0.4582684], [0.89919734, 0.40822271, 0.6298126 ]], #,[0.66389197, 0.49352551, 0.2987611], [0.09592603, 0.20024474, 0.67744112]
81
  'box_prompts': [[0.6, 0.72, 0.0, 0.75, 0.85, 0.6], [0.75, 0.70, 0.5, 0.92, 0.92, 0.75], [0.05, 0.92, 0.05, 0.27, 1.0, 0.82],
82
  [0.35, 0.03, 0.15, 0.5, 0.1, 0.42], ],
83
  'mask_prompts': [[0.86644632, 0.26297486, 0.5173167]],
 
86
 
87
  sample_3 = {'path': 'data/ScanNet/scene0016_02.pth',
88
  'point_prompts': [[0.2898192, 0.5845358, 0.7862434], [0.8251329,0.1763976,0.2942619]],
89
+ # [[0.77345204, 0.5883323, 0.21049459], [0.82484114, 0.16314957, 0.23850442], [0.97325081, 0.28361404, 0.15121479],
90
+ # [0.29043797, 0.58934051, 0.82521498], [0.46316043, 0.34840286, 0.01032902], [0.3637068, 0.50896871, 0.63058698]],
91
+ 'box_prompts': [[0.72, 0.36, 0.1, 0.9, 0.75, 0.75], [0.27, 0.54, 0.7, 0.3, 0.65, 0.9],], #[0.86, 0.12, 0.33, 0.99, 0.24, 0.54], [0.42, 0.5, 0.05, 0.55, 0.68, 0.42]
92
  'mask_prompts': [[0.2898192, 0.5845358, 0.7862434]],
93
  }
94
 
95
 
96
  sample_4 = {'path': 'data/ScanNet/scene0019_01.pth',
97
+ 'point_prompts': [[0.52182293, 0.69650459, 0.36580974], [0.6603151, 0.26341686, 0.33537653],[0.03188787, 0.65648252, 0.43863711]], #
98
+ # [0.79430991, 0.31488013, 0.2448331], [0.14427963, 0.69153076, 0.20673281], [0.17163187, 0.30585486, 0.31457961],
99
+ 'box_prompts': [[0.55, 0.22, 0.05, 0.72, 0.3, 0.58], [0.0, 0.27, 0.05, 0.2, 0.35, 0.45]], #[0.03, 0.59, 0.05, 0.2, 0.85, 0.35],
100
+ # [0.43, 0.65, 0.05, 0.64, 0.72, 0.65]],
101
  'mask_prompts': [[0.52182293, 0.69650459, 0.36580974], [0.6603151, 0.26341686, 0.33537653], [0.17163187, 0.30585486, 0.31457961], [0.03188787, 0.65648252, 0.43863711]],
102
  }
103
 
 
112
  'mask_prompts': [[0.56711978, 0.74271345, 0.1753805 ], [0.61877084, 0.47617316, 0.23380645]],
113
  }
114
 
115
+ ScanNet_samples = [sample_1, sample_2, sample_3, sample_4, sample_5, sample_6] #sample_0,
116
 
117
 
118
  sample_0 = {'path': 'data/Objaverse/plant.npy',
119
+ 'point_prompts': [[0.50455284, 0.47794762, 0.0007253083], [0.28331658, 0.19435011, 0.77393067]], #[7006, 1458],
120
  'voxel_size': [0.038, 0.04],
121
+ # 'voxel_size': [0.03, 0.04],
122
+ 'box_prompts': [[0.08, 0.18, -0.02, 0.68, 0.73, 0.315]], #, [0, 0, 0.3, 1, 1, 1.01]], #[0.11, 0.43, 0.82, 0.5, 1.01, 1.01]],
123
+ 'voxel_size_box': [0.04, 0.05], #0.01,
124
+ 'mask_prompts': [[0.50455284, 0.47794762, 0.0007253083]], #[7006, 1458], , [0.28331658, 0.19435011, 0.77393067]
125
  'voxel_size_mask': [0.038]
126
  }
127
 
128
 
129
  sample_1 = {'path': 'data/Objaverse/human.npy',
130
+ 'point_prompts': [[0.57825595, 0.5005686, 0.11494722], [0.7136412, 0.49501216, 0.5020814 ], [0.7136412, 0.49501216, 0.5020814 ]], #[1112, 2133, 2133],
131
  'voxel_size': [0.055, 0.045, 0.05],
132
  'box_prompts': [[0., 0.17, -0.01, 0.72, 0.80, 0.3], [-0.01, 0., 0.28, 0.8, 1, 0.82], [-0.01, 0.28, 0.89, 1, 0.72, 1.02]],
133
  'voxel_size_box': [0.055, 0.045, 0.055],
134
+ 'mask_prompts': [[0.57825595, 0.5005686, 0.11494722], [0.7136412, 0.49501216, 0.5020814 ]], #[1112, 2133, 2133],
135
  'voxel_size_mask': [0.055, 0.055],
136
  }
137
  sample_2 = {'path': 'data/Objaverse/lock.npy',
138
+ 'point_prompts': [[0.6513301, 0.6753892, 0.52316076], [0.21359734, 0.6097132 , 0.7939796 ], [0.44947368, 0.21654338, 0.58450174]], #[1029, 2064, 3541], #, [0.67447126, 0.6777649 , 0.51486933]
139
+ 'voxel_size': [0.04, 0.05, 0.05], #, 0.05
140
+ 'box_prompts': [[0.61, 0.4, 0.35, 0.8, 0.8, 0.6], [0.42, -0.02, -0.02, 1.02, 0.4, 1]], #[0., 0.25, -0.02, 0.4, 0.82, 1],
141
+ 'voxel_size_box': [0.04, 0.011], # 0.05, 0.04
142
+ 'mask_prompts': [[0.6513301, 0.6753892, 0.52316076], [0.21359734, 0.6097132 , 0.7939796 ], [0.9157764, 0.1995991, 0.14024617]], #[1029, 2064, 3541],
143
  'voxel_size_mask': [0.04, 0.055, 0.04],
144
  }
145
 
 
171
  }
172
 
173
  sample_6 = {'path': 'data/Objaverse/popcorn_machine.npy',
174
+ 'point_prompts': [[0.278306, 0.4913014, 0.7318756], [0.5867118, 0.1180351, 0.5844101]], #, [0.8857, 0.8296, 0.6090]],
175
  'voxel_size': [0.04, 0.04],
176
  'box_prompts': [[0.208, 0.157, 0.493, 0.779, 0.89, 0.925]],
177
  'voxel_size_box': [0.04],
178
+ 'mask_prompts': [[0.278306, 0.4913014, 0.7318756], [0.5867118, 0.1180351, 0.5844101]], #, [0.8857, 0.8296, 0.6090]],
179
  'voxel_size_mask': [0.04, 0.04],
180
  }
181
 
 
201
  sample_9 = {'path': 'data/Objaverse/thor_hammer.npy',
202
  'point_prompts': [[0.6211515, 0.5109989, 0.3867725], [0.44443, 0.2363458, 0.7229376]],
203
  'voxel_size': [0.05, 0.05, 0.05],
204
+ 'box_prompts': [[0,0,0.723,1,1,1]], #, [0.353, 0.41, 0, 0.636, 0.586, 0.725]],
205
  'voxel_size_box': [0.05, 0.05],
206
  'mask_prompts': [[0.44443, 0.2363458, 0.7229376]],
207
  'voxel_size_mask': [0.05],
 
210
  sample_10 = {'path': 'data/Objaverse/horse.npy',
211
  'point_prompts': [[0.3359364, 0.7555879, 0.6848574], [0.9221735, 0.1779197, 0.1927067]],
212
  'voxel_size': [0.04, 0.04],
213
+ 'box_prompts': [[0.65,0,0.3,1,1,0.79], [0.37, 0, 0, 1, 1, 0.2]], #, [0.353, 0.41, 0, 0.636, 0.586, 0.725]],
214
  'voxel_size_box': [0.04, 0.04],
215
  'mask_prompts': [[0.3359364, 0.7555879, 0.6848574], [0.9221735, 0.1779197, 0.1927067]],
216
  'voxel_size_mask': [0.04, 0.04],
 
221
  [0.9192697, 0.4469184, 0.0017635],
222
  [0.4987888, 0.6916906, 0.5106028]],
223
  'voxel_size': [0.04, 0.04],
224
+ 'box_prompts': [[0.65,0,0.3,1,1,0.79], [0.37, 0, 0, 1, 1, 0.2]], #, [0.353, 0.41, 0, 0.636, 0.586, 0.725]],
225
  'voxel_size_box': [0.04, 0.04],
226
  'mask_prompts': [[0.3359364, 0.7555879, 0.6848574], [0.9221735, 0.1779197, 0.1927067]],
227
  'voxel_size_mask': [0.04, 0.04],
228
  }
229
+ # sculpture.npy
230
+ # horse.npy
231
+ # pipe.npy
232
+ # dinner_booth.npy
233
+ # ornament.npy
234
+ # blender.npy
235
+ # bowl.npy
236
+ # human_face.npy
237
+ # table.npy
238
+ # telescope.npy
239
+ # planet.npy
240
+ # lamp.npy
241
+ # dragon.npy
242
 
243
  Objaverse_samples = [sample_0, sample_1, sample_2, sample_3, sample_4, sample_5, sample_6, sample_7, sample_8, sample_9, sample_10, sample_11]
244
+ # sample_1, sample_2,
245
+
246
 
247
 
248
  sample_0 = {'path': 'data/KITTI/scene1.npy',
 
351
  'voxel_size': [0.017, 0.017, 0.017, 0.017],
352
  'box_prompts': [],
353
  'voxel_size_box': [],
354
+ 'mask_prompts': [[0.1857393, 0.2675134, 0.2463012]], #[[0.51603703, 0.51312565, 0.50598845]],
355
+ 'voxel_size_mask': [0.01], #[0.01],
356
  }
357
 
358
  sample_5 = {'path': 'data/Semantic3D/patch50.npy',
 
377
  Semantic3D_samples = [sample_0, sample_1, sample_2, sample_3, sample_4, sample_5, sample_6]
378
 
379
 
380
+
381
+
382
+ VOXEL = {"point": "voxel_size", "box": "voxel_size_box", "mask": "voxel_size_mask"}
383
+