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queries/queries_nvidia-graphics-research.jsonl
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| 1 |
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{"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}
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| 2 |
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{"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}
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| 3 |
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{"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}
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| 4 |
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{"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}
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| 5 |
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{"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}
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| 6 |
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{"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}
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| 7 |
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{"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}
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| 8 |
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{"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}
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| 9 |
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{"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}
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| 10 |
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{"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}
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| 11 |
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{"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}
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| 12 |
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{"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}
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| 13 |
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{"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}
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| 14 |
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{"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}
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| 15 |
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{"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}
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| 16 |
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{"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}
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| 17 |
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{"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}
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| 18 |
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{"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}
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| 19 |
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{"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}
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| 20 |
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{"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}
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| 21 |
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{"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}
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| 22 |
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{"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}
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| 23 |
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{"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}
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| 24 |
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{"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}
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| 25 |
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{"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}
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| 26 |
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{"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}
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| 27 |
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{"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}
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| 28 |
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{"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}
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| 29 |
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{"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}
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| 30 |
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{"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}
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| 31 |
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{"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}
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| 32 |
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{"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}
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| 33 |
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{"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}
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| 34 |
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{"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}
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| 35 |
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{"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}
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| 36 |
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{"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}
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| 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}
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| 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}
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| 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}
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| 40 |
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{"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}
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| 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}
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| 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}
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| 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}
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| 44 |
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{"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}
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| 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}
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| 46 |
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{"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}
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| 47 |
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{"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}
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| 48 |
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{"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}
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| 49 |
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{"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}
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| 50 |
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{"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}
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| 51 |
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{"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}
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| 52 |
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{"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}
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| 53 |
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{"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}
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| 54 |
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{"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}
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| 55 |
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{"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}
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| 56 |
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{"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}
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| 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}
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| 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}
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| 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}
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| 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}
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| 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}
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| 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}
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| 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}
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| 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}
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| 65 |
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{"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}
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| 66 |
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{"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}
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| 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}
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| 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}
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| 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}
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| 70 |
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{"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}
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| 71 |
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{"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}
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| 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]}
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| 73 |
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{"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]}
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| 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}
|