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queries/queries_nvidia-graphics-research.jsonl ADDED
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1
+ {"id": "nvidia-graphics-research_T1_41", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is StyleGAN?", "ground_truth": ["StyleGAN", "FRAMEWOR"], "concept_id": 41, "hop_depth": 0}
2
+ {"id": "nvidia-graphics-research_T1_8", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is PBR Material?", "ground_truth": ["PBR Material", "CONCEPT"], "concept_id": 8, "hop_depth": 0}
3
+ {"id": "nvidia-graphics-research_T1_2", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Differentiable Rendering?", "ground_truth": ["Differentiable Rendering", "CONCEPT"], "concept_id": 2, "hop_depth": 0}
4
+ {"id": "nvidia-graphics-research_T1_18", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Alpha Compositing?", "ground_truth": ["Alpha Compositing", "ALGORITH"], "concept_id": 18, "hop_depth": 0}
5
+ {"id": "nvidia-graphics-research_T1_16", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Volume Rendering?", "ground_truth": ["Volume Rendering", "ALGORITH"], "concept_id": 16, "hop_depth": 0}
6
+ {"id": "nvidia-graphics-research_T1_15", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Multiresolution Hash Table?", "ground_truth": ["Multiresolution Hash Table", "DATA_STR"], "concept_id": 15, "hop_depth": 0}
7
+ {"id": "nvidia-graphics-research_T1_9", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Environment Map Lighting?", "ground_truth": ["Environment Map Lighting", "CONCEPT"], "concept_id": 9, "hop_depth": 0}
8
+ {"id": "nvidia-graphics-research_T1_7", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is nvdiffrecmc?", "ground_truth": ["nvdiffrecmc", "FRAMEWOR"], "concept_id": 7, "hop_depth": 0}
9
+ {"id": "nvidia-graphics-research_T1_35", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Custom CUDA Kernel Optimization?", "ground_truth": ["Custom CUDA Kernel Optimization", "TECHNIQU"], "concept_id": 35, "hop_depth": 0}
10
+ {"id": "nvidia-graphics-research_T1_6", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is nvdiffrec?", "ground_truth": ["nvdiffrec", "FRAMEWOR"], "concept_id": 6, "hop_depth": 0}
11
+ {"id": "nvidia-graphics-research_T1_28", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Point Cloud Operations?", "ground_truth": ["Point Cloud Operations", "MODULE"], "concept_id": 28, "hop_depth": 0}
12
+ {"id": "nvidia-graphics-research_T1_3", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Triangle Rasterization?", "ground_truth": ["Triangle Rasterization", "ALGORITH"], "concept_id": 3, "hop_depth": 0}
13
+ {"id": "nvidia-graphics-research_T1_44", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is eDiff-I?", "ground_truth": ["eDiff-I", "FRAMEWOR"], "concept_id": 44, "hop_depth": 0}
14
+ {"id": "nvidia-graphics-research_T1_37", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Signed Distance Function?", "ground_truth": ["Signed Distance Function", "REPRESEN"], "concept_id": 37, "hop_depth": 0}
15
+ {"id": "nvidia-graphics-research_T1_14", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Multi-Resolution Hash Encoding?", "ground_truth": ["Multi-Resolution Hash Encoding", "ALGORITH"], "concept_id": 14, "hop_depth": 0}
16
+ {"id": "nvidia-graphics-research_T1_45", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is NVlabs GitHub?", "ground_truth": ["NVlabs GitHub", "ORGANIZA"], "concept_id": 45, "hop_depth": 0}
17
+ {"id": "nvidia-graphics-research_T1_17", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Ray Marching?", "ground_truth": ["Ray Marching", "ALGORITH"], "concept_id": 17, "hop_depth": 0}
18
+ {"id": "nvidia-graphics-research_T1_20", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Opacity / Density Field?", "ground_truth": ["Opacity / Density Field", "REPRESEN"], "concept_id": 20, "hop_depth": 0}
19
+ {"id": "nvidia-graphics-research_T1_1", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is nvdiffrast?", "ground_truth": ["nvdiffrast", "LIBRARY"], "concept_id": 1, "hop_depth": 0}
20
+ {"id": "nvidia-graphics-research_T1_43", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Tri-Plane Representation?", "ground_truth": ["Tri-Plane Representation", "REPRESEN"], "concept_id": 43, "hop_depth": 0}
21
+ {"id": "nvidia-graphics-research_T1_39", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Feature Grid?", "ground_truth": ["Feature Grid", "DATA_STR"], "concept_id": 39, "hop_depth": 0}
22
+ {"id": "nvidia-graphics-research_T1_23", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Gaussian Rasterizer?", "ground_truth": ["Gaussian Rasterizer", "COMPONEN"], "concept_id": 23, "hop_depth": 0}
23
+ {"id": "nvidia-graphics-research_T1_21", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Deformable NeRF?", "ground_truth": ["Deformable NeRF", "FRAMEWOR"], "concept_id": 21, "hop_depth": 0}
24
+ {"id": "nvidia-graphics-research_T1_25", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Kaolin?", "ground_truth": ["Kaolin", "LIBRARY"], "concept_id": 25, "hop_depth": 0}
25
+ {"id": "nvidia-graphics-research_T1_27", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Mesh Operations?", "ground_truth": ["Mesh Operations", "MODULE"], "concept_id": 27, "hop_depth": 0}
26
+ {"id": "nvidia-graphics-research_T1_32", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is PyTorch?", "ground_truth": ["PyTorch", "PLATFORM"], "concept_id": 32, "hop_depth": 0}
27
+ {"id": "nvidia-graphics-research_T1_31", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Marching Cubes?", "ground_truth": ["Marching Cubes", "ALGORITH"], "concept_id": 31, "hop_depth": 0}
28
+ {"id": "nvidia-graphics-research_T1_46", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Coarse-to-Fine Training?", "ground_truth": ["Coarse-to-Fine Training", "STRATEGY"], "concept_id": 46, "hop_depth": 0}
29
+ {"id": "nvidia-graphics-research_T1_40", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is MLP Backbone?", "ground_truth": ["MLP Backbone", "COMPONEN"], "concept_id": 40, "hop_depth": 0}
30
+ {"id": "nvidia-graphics-research_T1_36", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Neural SDF?", "ground_truth": ["Neural SDF", "REPRESEN"], "concept_id": 36, "hop_depth": 0}
31
+ {"id": "nvidia-graphics-research_T1_33", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is 3D Scene Reconstruction?", "ground_truth": ["3D Scene Reconstruction", "TASK"], "concept_id": 33, "hop_depth": 0}
32
+ {"id": "nvidia-graphics-research_T1_12", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Instant-NGP?", "ground_truth": ["Instant-NGP", "FRAMEWOR"], "concept_id": 12, "hop_depth": 0}
33
+ {"id": "nvidia-graphics-research_T1_26", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Kaolin-Wisp?", "ground_truth": ["Kaolin-Wisp", "FRAMEWOR"], "concept_id": 26, "hop_depth": 0}
34
+ {"id": "nvidia-graphics-research_T1_42", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is EG3D?", "ground_truth": ["EG3D", "FRAMEWOR"], "concept_id": 42, "hop_depth": 0}
35
+ {"id": "nvidia-graphics-research_T1_5", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Inverse Rendering?", "ground_truth": ["Inverse Rendering", "TASK"], "concept_id": 5, "hop_depth": 0}
36
+ {"id": "nvidia-graphics-research_T1_38", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Occupancy Networks?", "ground_truth": ["Occupancy Networks", "REPRESEN"], "concept_id": 38, "hop_depth": 0}
37
+ {"id": "nvidia-graphics-research_T1_4", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Differentiable Antialiasing?", "ground_truth": ["Differentiable Antialiasing", "ALGORITH"], "concept_id": 4, "hop_depth": 0}
38
+ {"id": "nvidia-graphics-research_T1_13", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is NeRF?", "ground_truth": ["NeRF", "REPRESEN"], "concept_id": 13, "hop_depth": 0}
39
+ {"id": "nvidia-graphics-research_T1_34", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is tiny-cuda-nn?", "ground_truth": ["tiny-cuda-nn", "LIBRARY"], "concept_id": 34, "hop_depth": 0}
40
+ {"id": "nvidia-graphics-research_T1_30", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Mesh-to-SDF Conversion?", "ground_truth": ["Mesh-to-SDF Conversion", "ALGORITH"], "concept_id": 30, "hop_depth": 0}
41
+ {"id": "nvidia-graphics-research_T1_10", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Neural Texture?", "ground_truth": ["Neural Texture", "REPRESEN"], "concept_id": 10, "hop_depth": 0}
42
+ {"id": "nvidia-graphics-research_T1_24", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Spherical Harmonics?", "ground_truth": ["Spherical Harmonics", "TECHNIQU"], "concept_id": 24, "hop_depth": 0}
43
+ {"id": "nvidia-graphics-research_T1_11", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Neural Importance Sampling?", "ground_truth": ["Neural Importance Sampling", "ALGORITH"], "concept_id": 11, "hop_depth": 0}
44
+ {"id": "nvidia-graphics-research_T1_29", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Chamfer Distance?", "ground_truth": ["Chamfer Distance", "METRIC"], "concept_id": 29, "hop_depth": 0}
45
+ {"id": "nvidia-graphics-research_T1_22", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is 3D Gaussian Splatting?", "ground_truth": ["3D Gaussian Splatting", "FRAMEWOR"], "concept_id": 22, "hop_depth": 0}
46
+ {"id": "nvidia-graphics-research_T1_19", "domain": "nvidia-graphics-research", "type": "T1_entity", "query": "What is Appearance Model?", "ground_truth": ["Appearance Model", "CONCEPT"], "concept_id": 19, "hop_depth": 0}
47
+ {"id": "nvidia-graphics-research_T2_42", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for EG3D?", "ground_truth": ["Volume Rendering", "StyleGAN", "Tri-Plane Representation"], "concept_id": 42, "hop_depth": 1}
48
+ {"id": "nvidia-graphics-research_T2_25", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Kaolin?", "ground_truth": ["PyTorch"], "concept_id": 25, "hop_depth": 1}
49
+ {"id": "nvidia-graphics-research_T2_30", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Mesh-to-SDF Conversion?", "ground_truth": ["Mesh Operations"], "concept_id": 30, "hop_depth": 1}
50
+ {"id": "nvidia-graphics-research_T2_8", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for PBR Material?", "ground_truth": ["Environment Map Lighting"], "concept_id": 8, "hop_depth": 1}
51
+ {"id": "nvidia-graphics-research_T2_22", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for 3D Gaussian Splatting?", "ground_truth": ["Gaussian Rasterizer"], "concept_id": 22, "hop_depth": 1}
52
+ {"id": "nvidia-graphics-research_T2_7", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for nvdiffrecmc?", "ground_truth": ["nvdiffrec", "nvdiffrast", "Neural Importance Sampling"], "concept_id": 7, "hop_depth": 1}
53
+ {"id": "nvidia-graphics-research_T2_40", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for MLP Backbone?", "ground_truth": ["Custom CUDA Kernel Optimization"], "concept_id": 40, "hop_depth": 1}
54
+ {"id": "nvidia-graphics-research_T2_19", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Appearance Model?", "ground_truth": ["MLP Backbone"], "concept_id": 19, "hop_depth": 1}
55
+ {"id": "nvidia-graphics-research_T2_21", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Deformable NeRF?", "ground_truth": ["NeRF"], "concept_id": 21, "hop_depth": 1}
56
+ {"id": "nvidia-graphics-research_T2_13", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for NeRF?", "ground_truth": ["Volume Rendering", "Appearance Model"], "concept_id": 13, "hop_depth": 1}
57
+ {"id": "nvidia-graphics-research_T2_28", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Point Cloud Operations?", "ground_truth": ["Chamfer Distance"], "concept_id": 28, "hop_depth": 1}
58
+ {"id": "nvidia-graphics-research_T2_6", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for nvdiffrec?", "ground_truth": ["nvdiffrast", "Inverse Rendering"], "concept_id": 6, "hop_depth": 1}
59
+ {"id": "nvidia-graphics-research_T2_1", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for nvdiffrast?", "ground_truth": ["Triangle Rasterization"], "concept_id": 1, "hop_depth": 1}
60
+ {"id": "nvidia-graphics-research_T2_20", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Opacity / Density Field?", "ground_truth": ["NeRF"], "concept_id": 20, "hop_depth": 1}
61
+ {"id": "nvidia-graphics-research_T2_39", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Feature Grid?", "ground_truth": ["Multiresolution Hash Table"], "concept_id": 39, "hop_depth": 1}
62
+ {"id": "nvidia-graphics-research_T2_10", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Neural Texture?", "ground_truth": ["PBR Material"], "concept_id": 10, "hop_depth": 1}
63
+ {"id": "nvidia-graphics-research_T2_23", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Gaussian Rasterizer?", "ground_truth": ["Triangle Rasterization"], "concept_id": 23, "hop_depth": 1}
64
+ {"id": "nvidia-graphics-research_T2_43", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Tri-Plane Representation?", "ground_truth": ["Triangle Rasterization"], "concept_id": 43, "hop_depth": 1}
65
+ {"id": "nvidia-graphics-research_T2_26", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Kaolin-Wisp?", "ground_truth": ["Kaolin", "tiny-cuda-nn", "Neural SDF", "NeRF"], "concept_id": 26, "hop_depth": 1}
66
+ {"id": "nvidia-graphics-research_T2_38", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Occupancy Networks?", "ground_truth": ["MLP Backbone"], "concept_id": 38, "hop_depth": 1}
67
+ {"id": "nvidia-graphics-research_T2_14", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Multi-Resolution Hash Encoding?", "ground_truth": ["Feature Grid", "Multiresolution Hash Table"], "concept_id": 14, "hop_depth": 1}
68
+ {"id": "nvidia-graphics-research_T2_36", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Neural SDF?", "ground_truth": ["Signed Distance Function"], "concept_id": 36, "hop_depth": 1}
69
+ {"id": "nvidia-graphics-research_T2_16", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Volume Rendering?", "ground_truth": ["Ray Marching", "Alpha Compositing"], "concept_id": 16, "hop_depth": 1}
70
+ {"id": "nvidia-graphics-research_T2_34", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for tiny-cuda-nn?", "ground_truth": ["Custom CUDA Kernel Optimization"], "concept_id": 34, "hop_depth": 1}
71
+ {"id": "nvidia-graphics-research_T2_12", "domain": "nvidia-graphics-research", "type": "T2_dependency", "query": "What are the prerequisites for Instant-NGP?", "ground_truth": ["tiny-cuda-nn", "NeRF", "Multi-Resolution Hash Encoding"], "concept_id": 12, "hop_depth": 1}
72
+ {"id": "nvidia-graphics-research_T3_3_22", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Triangle Rasterization to 3D Gaussian Splatting?", "ground_truth": ["3D Gaussian Splatting", "Gaussian Rasterizer", "Triangle Rasterization"], "concept_id": 22, "hop_depth": 2, "path_ids": [22, 23, 3]}
73
+ {"id": "nvidia-graphics-research_T3_17_12", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Ray Marching to Instant-NGP?", "ground_truth": ["Instant-NGP", "NeRF", "Volume Rendering", "Ray Marching"], "concept_id": 12, "hop_depth": 3, "path_ids": [12, 13, 16, 17]}
74
+ {"id": "nvidia-graphics-research_T3_3_22", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Triangle Rasterization to 3D Gaussian Splatting?", "ground_truth": ["3D Gaussian Splatting", "Gaussian Rasterizer", "Triangle Rasterization"], "concept_id": 22, "hop_depth": 2, "path_ids": [22, 23, 3]}
75
+ {"id": "nvidia-graphics-research_T3_35_21", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Custom CUDA Kernel Optimization to Deformable NeRF?", "ground_truth": ["Deformable NeRF", "NeRF", "Appearance Model", "MLP Backbone", "Custom CUDA Kernel Optimization"], "concept_id": 21, "hop_depth": 4, "path_ids": [21, 13, 19, 40, 35]}
76
+ {"id": "nvidia-graphics-research_T3_17_21", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Ray Marching to Deformable NeRF?", "ground_truth": ["Deformable NeRF", "NeRF", "Volume Rendering", "Ray Marching"], "concept_id": 21, "hop_depth": 3, "path_ids": [21, 13, 16, 17]}
77
+ {"id": "nvidia-graphics-research_T3_17_20", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Ray Marching to Opacity / Density Field?", "ground_truth": ["Opacity / Density Field", "NeRF", "Volume Rendering", "Ray Marching"], "concept_id": 20, "hop_depth": 3, "path_ids": [20, 13, 16, 17]}
78
+ {"id": "nvidia-graphics-research_T3_35_21", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Custom CUDA Kernel Optimization to Deformable NeRF?", "ground_truth": ["Deformable NeRF", "NeRF", "Appearance Model", "MLP Backbone", "Custom CUDA Kernel Optimization"], "concept_id": 21, "hop_depth": 4, "path_ids": [21, 13, 19, 40, 35]}
79
+ {"id": "nvidia-graphics-research_T3_41_42", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from StyleGAN to EG3D?", "ground_truth": ["EG3D", "StyleGAN"], "concept_id": 42, "hop_depth": 1, "path_ids": [42, 41]}
80
+ {"id": "nvidia-graphics-research_T3_5_7", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Inverse Rendering to nvdiffrecmc?", "ground_truth": ["nvdiffrecmc", "nvdiffrec", "Inverse Rendering"], "concept_id": 7, "hop_depth": 2, "path_ids": [7, 6, 5]}
81
+ {"id": "nvidia-graphics-research_T3_18_26", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Alpha Compositing to Kaolin-Wisp?", "ground_truth": ["Kaolin-Wisp", "NeRF", "Volume Rendering", "Alpha Compositing"], "concept_id": 26, "hop_depth": 3, "path_ids": [26, 13, 16, 18]}
82
+ {"id": "nvidia-graphics-research_T3_5_7", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Inverse Rendering to nvdiffrecmc?", "ground_truth": ["nvdiffrecmc", "nvdiffrec", "Inverse Rendering"], "concept_id": 7, "hop_depth": 2, "path_ids": [7, 6, 5]}
83
+ {"id": "nvidia-graphics-research_T3_17_20", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Ray Marching to Opacity / Density Field?", "ground_truth": ["Opacity / Density Field", "NeRF", "Volume Rendering", "Ray Marching"], "concept_id": 20, "hop_depth": 3, "path_ids": [20, 13, 16, 17]}
84
+ {"id": "nvidia-graphics-research_T3_29_28", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Chamfer Distance to Point Cloud Operations?", "ground_truth": ["Point Cloud Operations", "Chamfer Distance"], "concept_id": 28, "hop_depth": 1, "path_ids": [28, 29]}
85
+ {"id": "nvidia-graphics-research_T3_37_26", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Signed Distance Function to Kaolin-Wisp?", "ground_truth": ["Kaolin-Wisp", "Neural SDF", "Signed Distance Function"], "concept_id": 26, "hop_depth": 2, "path_ids": [26, 36, 37]}
86
+ {"id": "nvidia-graphics-research_T3_11_7", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Neural Importance Sampling to nvdiffrecmc?", "ground_truth": ["nvdiffrecmc", "Neural Importance Sampling"], "concept_id": 7, "hop_depth": 1, "path_ids": [7, 11]}
87
+ {"id": "nvidia-graphics-research_T3_15_12", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Multiresolution Hash Table to Instant-NGP?", "ground_truth": ["Instant-NGP", "Multi-Resolution Hash Encoding", "Multiresolution Hash Table"], "concept_id": 12, "hop_depth": 2, "path_ids": [12, 14, 15]}
88
+ {"id": "nvidia-graphics-research_T3_17_42", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Ray Marching to EG3D?", "ground_truth": ["EG3D", "Volume Rendering", "Ray Marching"], "concept_id": 42, "hop_depth": 2, "path_ids": [42, 16, 17]}
89
+ {"id": "nvidia-graphics-research_T3_17_12", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Ray Marching to Instant-NGP?", "ground_truth": ["Instant-NGP", "NeRF", "Volume Rendering", "Ray Marching"], "concept_id": 12, "hop_depth": 3, "path_ids": [12, 13, 16, 17]}
90
+ {"id": "nvidia-graphics-research_T3_17_20", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Ray Marching to Opacity / Density Field?", "ground_truth": ["Opacity / Density Field", "NeRF", "Volume Rendering", "Ray Marching"], "concept_id": 20, "hop_depth": 3, "path_ids": [20, 13, 16, 17]}
91
+ {"id": "nvidia-graphics-research_T3_35_38", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Custom CUDA Kernel Optimization to Occupancy Networks?", "ground_truth": ["Occupancy Networks", "MLP Backbone", "Custom CUDA Kernel Optimization"], "concept_id": 38, "hop_depth": 2, "path_ids": [38, 40, 35]}
92
+ {"id": "nvidia-graphics-research_T3_17_12", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Ray Marching to Instant-NGP?", "ground_truth": ["Instant-NGP", "NeRF", "Volume Rendering", "Ray Marching"], "concept_id": 12, "hop_depth": 3, "path_ids": [12, 13, 16, 17]}
93
+ {"id": "nvidia-graphics-research_T3_35_38", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Custom CUDA Kernel Optimization to Occupancy Networks?", "ground_truth": ["Occupancy Networks", "MLP Backbone", "Custom CUDA Kernel Optimization"], "concept_id": 38, "hop_depth": 2, "path_ids": [38, 40, 35]}
94
+ {"id": "nvidia-graphics-research_T3_17_26", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Ray Marching to Kaolin-Wisp?", "ground_truth": ["Kaolin-Wisp", "NeRF", "Volume Rendering", "Ray Marching"], "concept_id": 26, "hop_depth": 3, "path_ids": [26, 13, 16, 17]}
95
+ {"id": "nvidia-graphics-research_T3_27_30", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Mesh Operations to Mesh-to-SDF Conversion?", "ground_truth": ["Mesh-to-SDF Conversion", "Mesh Operations"], "concept_id": 30, "hop_depth": 1, "path_ids": [30, 27]}
96
+ {"id": "nvidia-graphics-research_T3_37_26", "domain": "nvidia-graphics-research", "type": "T3_path", "query": "What is the prerequisite chain from Signed Distance Function to Kaolin-Wisp?", "ground_truth": ["Kaolin-Wisp", "Neural SDF", "Signed Distance Function"], "concept_id": 26, "hop_depth": 2, "path_ids": [26, 36, 37]}
97
+ {"id": "nvidia-graphics-research_T4_LIBRARY", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all LIBRARY concepts in this knowledge graph", "ground_truth": ["nvdiffrast", "Kaolin", "tiny-cuda-nn"], "taxonomy_id": "LIBRARY", "hop_depth": 0}
98
+ {"id": "nvidia-graphics-research_T4_CONCEPT", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all CONCEPT concepts in this knowledge graph", "ground_truth": ["Differentiable Rendering", "PBR Material", "Environment Map Lighting", "Appearance Model"], "taxonomy_id": "CONCEPT", "hop_depth": 0}
99
+ {"id": "nvidia-graphics-research_T4_ALGORITH", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all ALGORITH concepts in this knowledge graph", "ground_truth": ["Triangle Rasterization", "Differentiable Antialiasing", "Neural Importance Sampling", "Multi-Resolution Hash Encoding", "Volume Rendering", "Ray Marching", "Alpha Compositing", "Mesh-to-SDF Conversion", "Marching Cubes"], "taxonomy_id": "ALGORITH", "hop_depth": 0}
100
+ {"id": "nvidia-graphics-research_T4_TASK", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all TASK concepts in this knowledge graph", "ground_truth": ["Inverse Rendering", "3D Scene Reconstruction"], "taxonomy_id": "TASK", "hop_depth": 0}
101
+ {"id": "nvidia-graphics-research_T4_FRAMEWOR", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all FRAMEWOR concepts in this knowledge graph", "ground_truth": ["nvdiffrec", "nvdiffrecmc", "Instant-NGP", "Deformable NeRF", "3D Gaussian Splatting", "Kaolin-Wisp", "StyleGAN", "EG3D", "eDiff-I"], "taxonomy_id": "FRAMEWOR", "hop_depth": 0}
102
+ {"id": "nvidia-graphics-research_T4_REPRESEN", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all REPRESEN concepts in this knowledge graph", "ground_truth": ["Neural Texture", "NeRF", "Opacity / Density Field", "Neural SDF", "Signed Distance Function", "Occupancy Networks", "Tri-Plane Representation"], "taxonomy_id": "REPRESEN", "hop_depth": 0}
103
+ {"id": "nvidia-graphics-research_T4_DATA_STR", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all DATA_STR concepts in this knowledge graph", "ground_truth": ["Multiresolution Hash Table", "Feature Grid"], "taxonomy_id": "DATA_STR", "hop_depth": 0}
104
+ {"id": "nvidia-graphics-research_T4_COMPONEN", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all COMPONEN concepts in this knowledge graph", "ground_truth": ["Gaussian Rasterizer", "MLP Backbone"], "taxonomy_id": "COMPONEN", "hop_depth": 0}
105
+ {"id": "nvidia-graphics-research_T4_TECHNIQU", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all TECHNIQU concepts in this knowledge graph", "ground_truth": ["Spherical Harmonics", "Custom CUDA Kernel Optimization"], "taxonomy_id": "TECHNIQU", "hop_depth": 0}
106
+ {"id": "nvidia-graphics-research_T4_MODULE", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all MODULE concepts in this knowledge graph", "ground_truth": ["Mesh Operations", "Point Cloud Operations"], "taxonomy_id": "MODULE", "hop_depth": 0}
107
+ {"id": "nvidia-graphics-research_T4_METRIC", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all METRIC concepts in this knowledge graph", "ground_truth": ["Chamfer Distance"], "taxonomy_id": "METRIC", "hop_depth": 0}
108
+ {"id": "nvidia-graphics-research_T4_PLATFORM", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all PLATFORM concepts in this knowledge graph", "ground_truth": ["PyTorch"], "taxonomy_id": "PLATFORM", "hop_depth": 0}
109
+ {"id": "nvidia-graphics-research_T4_ORGANIZA", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all ORGANIZA concepts in this knowledge graph", "ground_truth": ["NVlabs GitHub"], "taxonomy_id": "ORGANIZA", "hop_depth": 0}
110
+ {"id": "nvidia-graphics-research_T4_STRATEGY", "domain": "nvidia-graphics-research", "type": "T4_aggregate", "query": "List all STRATEGY concepts in this knowledge graph", "ground_truth": ["Coarse-to-Fine Training"], "taxonomy_id": "STRATEGY", "hop_depth": 0}
111
+ {"id": "nvidia-graphics-research_T5_8_9", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does PBR Material relate to Environment Map Lighting?", "ground_truth": ["PBR Material", "Environment Map Lighting"], "concept_id_a": 8, "concept_id_b": 9, "hop_depth": 1}
112
+ {"id": "nvidia-graphics-research_T5_13_19", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does NeRF relate to Appearance Model?", "ground_truth": ["NeRF", "Appearance Model"], "concept_id_a": 13, "concept_id_b": 19, "hop_depth": 1}
113
+ {"id": "nvidia-graphics-research_T5_20_13", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Opacity / Density Field relate to NeRF?", "ground_truth": ["Opacity / Density Field", "NeRF"], "concept_id_a": 20, "concept_id_b": 13, "hop_depth": 1}
114
+ {"id": "nvidia-graphics-research_T5_14_15", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Multi-Resolution Hash Encoding relate to Multiresolution Hash Table?", "ground_truth": ["Multi-Resolution Hash Encoding", "Multiresolution Hash Table"], "concept_id_a": 14, "concept_id_b": 15, "hop_depth": 1}
115
+ {"id": "nvidia-graphics-research_T5_26_25", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Kaolin-Wisp relate to Kaolin?", "ground_truth": ["Kaolin-Wisp", "Kaolin"], "concept_id_a": 26, "concept_id_b": 25, "hop_depth": 1}
116
+ {"id": "nvidia-graphics-research_T5_13_16", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does NeRF relate to Volume Rendering?", "ground_truth": ["NeRF", "Volume Rendering"], "concept_id_a": 13, "concept_id_b": 16, "hop_depth": 1}
117
+ {"id": "nvidia-graphics-research_T5_21_13", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Deformable NeRF relate to NeRF?", "ground_truth": ["Deformable NeRF", "NeRF"], "concept_id_a": 21, "concept_id_b": 13, "hop_depth": 1}
118
+ {"id": "nvidia-graphics-research_T5_1_3", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does nvdiffrast relate to Triangle Rasterization?", "ground_truth": ["nvdiffrast", "Triangle Rasterization"], "concept_id_a": 1, "concept_id_b": 3, "hop_depth": 1}
119
+ {"id": "nvidia-graphics-research_T5_25_32", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Kaolin relate to PyTorch?", "ground_truth": ["Kaolin", "PyTorch"], "concept_id_a": 25, "concept_id_b": 32, "hop_depth": 1}
120
+ {"id": "nvidia-graphics-research_T5_19_40", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Appearance Model relate to MLP Backbone?", "ground_truth": ["Appearance Model", "MLP Backbone"], "concept_id_a": 19, "concept_id_b": 40, "hop_depth": 1}
121
+ {"id": "nvidia-graphics-research_T5_7_1", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does nvdiffrecmc relate to nvdiffrast?", "ground_truth": ["nvdiffrecmc", "nvdiffrast"], "concept_id_a": 7, "concept_id_b": 1, "hop_depth": 1}
122
+ {"id": "nvidia-graphics-research_T5_12_34", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Instant-NGP relate to tiny-cuda-nn?", "ground_truth": ["Instant-NGP", "tiny-cuda-nn"], "concept_id_a": 12, "concept_id_b": 34, "hop_depth": 1}
123
+ {"id": "nvidia-graphics-research_T5_6_1", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does nvdiffrec relate to nvdiffrast?", "ground_truth": ["nvdiffrec", "nvdiffrast"], "concept_id_a": 6, "concept_id_b": 1, "hop_depth": 1}
124
+ {"id": "nvidia-graphics-research_T5_26_36", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Kaolin-Wisp relate to Neural SDF?", "ground_truth": ["Kaolin-Wisp", "Neural SDF"], "concept_id_a": 26, "concept_id_b": 36, "hop_depth": 1}
125
+ {"id": "nvidia-graphics-research_T5_30_27", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Mesh-to-SDF Conversion relate to Mesh Operations?", "ground_truth": ["Mesh-to-SDF Conversion", "Mesh Operations"], "concept_id_a": 30, "concept_id_b": 27, "hop_depth": 1}
126
+ {"id": "nvidia-graphics-research_T5_12_13", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Instant-NGP relate to NeRF?", "ground_truth": ["Instant-NGP", "NeRF"], "concept_id_a": 12, "concept_id_b": 13, "hop_depth": 1}
127
+ {"id": "nvidia-graphics-research_T5_28_29", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Point Cloud Operations relate to Chamfer Distance?", "ground_truth": ["Point Cloud Operations", "Chamfer Distance"], "concept_id_a": 28, "concept_id_b": 29, "hop_depth": 1}
128
+ {"id": "nvidia-graphics-research_T5_22_23", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does 3D Gaussian Splatting relate to Gaussian Rasterizer?", "ground_truth": ["3D Gaussian Splatting", "Gaussian Rasterizer"], "concept_id_a": 22, "concept_id_b": 23, "hop_depth": 1}
129
+ {"id": "nvidia-graphics-research_T5_16_17", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Volume Rendering relate to Ray Marching?", "ground_truth": ["Volume Rendering", "Ray Marching"], "concept_id_a": 16, "concept_id_b": 17, "hop_depth": 1}
130
+ {"id": "nvidia-graphics-research_T5_7_6", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does nvdiffrecmc relate to nvdiffrec?", "ground_truth": ["nvdiffrecmc", "nvdiffrec", "nvdiffrast"], "concept_id_a": 7, "concept_id_b": 6, "hop_depth": 1}
131
+ {"id": "nvidia-graphics-research_T5_36_37", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Neural SDF relate to Signed Distance Function?", "ground_truth": ["Neural SDF", "Signed Distance Function"], "concept_id_a": 36, "concept_id_b": 37, "hop_depth": 1}
132
+ {"id": "nvidia-graphics-research_T5_34_35", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does tiny-cuda-nn relate to Custom CUDA Kernel Optimization?", "ground_truth": ["tiny-cuda-nn", "Custom CUDA Kernel Optimization"], "concept_id_a": 34, "concept_id_b": 35, "hop_depth": 1}
133
+ {"id": "nvidia-graphics-research_T5_38_40", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Occupancy Networks relate to MLP Backbone?", "ground_truth": ["Occupancy Networks", "MLP Backbone"], "concept_id_a": 38, "concept_id_b": 40, "hop_depth": 1}
134
+ {"id": "nvidia-graphics-research_T5_10_8", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Neural Texture relate to PBR Material?", "ground_truth": ["Neural Texture", "PBR Material"], "concept_id_a": 10, "concept_id_b": 8, "hop_depth": 1}
135
+ {"id": "nvidia-graphics-research_T5_42_16", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does EG3D relate to Volume Rendering?", "ground_truth": ["EG3D", "Volume Rendering"], "concept_id_a": 42, "concept_id_b": 16, "hop_depth": 1}
136
+ {"id": "nvidia-graphics-research_T5_7_11", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does nvdiffrecmc relate to Neural Importance Sampling?", "ground_truth": ["nvdiffrecmc", "Neural Importance Sampling"], "concept_id_a": 7, "concept_id_b": 11, "hop_depth": 1}
137
+ {"id": "nvidia-graphics-research_T5_6_5", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does nvdiffrec relate to Inverse Rendering?", "ground_truth": ["nvdiffrec", "Inverse Rendering"], "concept_id_a": 6, "concept_id_b": 5, "hop_depth": 1}
138
+ {"id": "nvidia-graphics-research_T5_40_35", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does MLP Backbone relate to Custom CUDA Kernel Optimization?", "ground_truth": ["MLP Backbone", "Custom CUDA Kernel Optimization"], "concept_id_a": 40, "concept_id_b": 35, "hop_depth": 1}
139
+ {"id": "nvidia-graphics-research_T5_23_3", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Gaussian Rasterizer relate to Triangle Rasterization?", "ground_truth": ["Gaussian Rasterizer", "Triangle Rasterization"], "concept_id_a": 23, "concept_id_b": 3, "hop_depth": 1}
140
+ {"id": "nvidia-graphics-research_T5_14_39", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Multi-Resolution Hash Encoding relate to Feature Grid?", "ground_truth": ["Multi-Resolution Hash Encoding", "Feature Grid", "Multiresolution Hash Table"], "concept_id_a": 14, "concept_id_b": 39, "hop_depth": 1}
141
+ {"id": "nvidia-graphics-research_T5_26_34", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Kaolin-Wisp relate to tiny-cuda-nn?", "ground_truth": ["Kaolin-Wisp", "tiny-cuda-nn"], "concept_id_a": 26, "concept_id_b": 34, "hop_depth": 1}
142
+ {"id": "nvidia-graphics-research_T5_43_3", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Tri-Plane Representation relate to Triangle Rasterization?", "ground_truth": ["Tri-Plane Representation", "Triangle Rasterization"], "concept_id_a": 43, "concept_id_b": 3, "hop_depth": 1}
143
+ {"id": "nvidia-graphics-research_T5_12_14", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Instant-NGP relate to Multi-Resolution Hash Encoding?", "ground_truth": ["Instant-NGP", "Multi-Resolution Hash Encoding"], "concept_id_a": 12, "concept_id_b": 14, "hop_depth": 1}
144
+ {"id": "nvidia-graphics-research_T5_42_41", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does EG3D relate to StyleGAN?", "ground_truth": ["EG3D", "StyleGAN"], "concept_id_a": 42, "concept_id_b": 41, "hop_depth": 1}
145
+ {"id": "nvidia-graphics-research_T5_16_18", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Volume Rendering relate to Alpha Compositing?", "ground_truth": ["Volume Rendering", "Alpha Compositing"], "concept_id_a": 16, "concept_id_b": 18, "hop_depth": 1}
146
+ {"id": "nvidia-graphics-research_T5_42_43", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does EG3D relate to Tri-Plane Representation?", "ground_truth": ["EG3D", "Tri-Plane Representation"], "concept_id_a": 42, "concept_id_b": 43, "hop_depth": 1}
147
+ {"id": "nvidia-graphics-research_T5_39_15", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Feature Grid relate to Multiresolution Hash Table?", "ground_truth": ["Feature Grid", "Multiresolution Hash Table"], "concept_id_a": 39, "concept_id_b": 15, "hop_depth": 1}
148
+ {"id": "nvidia-graphics-research_T5_26_13", "domain": "nvidia-graphics-research", "type": "T5_cross_concept", "query": "How does Kaolin-Wisp relate to NeRF?", "ground_truth": ["Kaolin-Wisp", "NeRF"], "concept_id_a": 26, "concept_id_b": 13, "hop_depth": 1}