Datasets:
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"field_id": "F003",
"field": "3d object detection",
"directions": [
{
"direction_id": "G0",
"name": "3D detection from camera geometry",
"scope": "Infer the 3D position and shape needed for detection from camera images, including depth and geometry inference from a single image, shared 3D representations built from multiple views, and depth estimation through explicit binocular or cross-temporal image correspondence."
},
{
"direction_id": "G1",
"name": "Detection evidence from range measurements",
"scope": "Make spatial evidence from point-cloud or radar measurements sufficient for 3D object detection by learning geometric features from observed points, compensating for incomplete evidence caused by sparsity, distance, or occlusion, and addressing radar-specific sparsity, noise, and encoding."
},
{
"direction_id": "G2",
"name": "Computationally feasible 3D detection",
"scope": "Reduce the computational and memory costs of detection representations, attention, or spatial processing while examining their detection effectiveness."
},
{
"direction_id": "G3",
"name": "Cross-sensor evidence fusion for detection",
"scope": "Associate, align, and combine complementary evidence actually available from different sensors during detection to improve 3D localization and recognition."
},
{
"direction_id": "G4",
"name": "Temporal evidence for 3D detections",
"scope": "Align, retain, or propagate evidence from past frames, scans, and object motion to improve detections, rather than making identity maintenance the primary output."
},
{
"direction_id": "G5",
"name": "Supplementary training evidence for 3D detectors",
"scope": "Improve detectors using knowledge or supervision beyond direct training on 3D boxes, including teacher-to-student transfer of detection knowledge, detection-oriented representation pre-training, and training with weak constraints and pseudo-labels when 3D box annotations are limited or absent."
},
{
"direction_id": "G6",
"name": "Detection across shifted and degraded conditions",
"scope": "Study how trained detectors remain effective when target conditions change: address training-to-deployment distribution differences in scenes, sensor sampling, or object geometry, and address corrupted, missing, adverse, or attacked input observations."
},
{
"direction_id": "G7",
"name": "Assistance with 3D box annotation",
"scope": "Reduce the human effort required to create or correct 3D object-box annotations, with usable annotations—rather than a detector trained on a fixed annotation set—as the primary output."
}
]
}
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