Research map / Label-efficient and robust learning / Self-supervised and contrastive
Identifying Good Training Data for Self-Supervised Free Space Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use proposed v-disparity and Bayesian-regression extraction instead of plane fitting | ν-SVC free-space estimation · Difficult | recall: 0.5784 -> 0.8147 | Helped |
| Use proposed extraction instead of plane fitting | ν-SVC free-space estimation · Easy | recall: 0.9646 -> 0.8671 | Hurt |
| Use proposed extraction instead of plane fitting | ν-SVC free-space estimation · Medium | recall: 0.9422 -> 0.8514 | Hurt |
| Use proposed extraction instead of plane fitting | ν-SVC free-space estimation · Easy | precision: 0.9340 -> 0.9604 | Helped |
| Use proposed extraction instead of plane fitting | ν-SVC free-space estimation · Medium | precision: 0.8931 -> 0.9326 | Helped |
| Use proposed extraction instead of plane fitting | ν-SVC free-space estimation · Difficult | precision: 0.9960 -> 0.9855 | Hurt |
| Use proposed extraction instead of plane fitting | ν-SVC free-space estimation · Easy | specificity: 0.9731 -> 0.9853 | Helped |
| Use proposed extraction instead of plane fitting | ν-SVC free-space estimation · Medium | specificity: 0.9592 -> 0.9781 | Helped |
| Use proposed extraction instead of plane fitting | ν-SVC free-space estimation · Difficult | specificity: 0.9957 -> 0.9725 | Hurt |
| Use proposed extraction instead of plane fitting | Free-space training-data extraction · Easy | extraction time (seconds): 0.1681 -> 0.0547 | Helped |
| Use proposed extraction instead of plane fitting | Free-space training-data extraction · Medium | extraction time (seconds): 0.4833 -> 0.0592 | Helped |
| Use proposed extraction instead of plane fitting | Free-space training-data extraction · Difficult | extraction time (seconds): 1.1138 -> 0.0598 | Helped |
Closest papers by meaning
- Single-Stage 3D Geometry-Preserving Depth Estimation Model Training on Dataset Mixtures With Uncalibrated Stereo Data (CVPR 2022 · similarity 0.83)
- Self-Supervised Monocular Trained Depth Estimation Using Self-Attention and Discrete Disparity Volume (CVPR 2020 · similarity 0.82)
- From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Prior (CVPR 2024 · similarity 0.80)
- Synthetic-to-Real Self-supervised Robust Depth Estimation via Learning with Motion and Structure Priors (CVPR 2025 · similarity 0.80)
- Any-Stereo: Arbitrary Scale Disparity Estimation for Iterative Stereo Matching (AAAI 2024 · similarity 0.80)
- GeoDepth: From Point-to-Depth to Plane-to-Depth Modeling for Self-Supervised Monocular Depth Estimation (CVPR 2025 · similarity 0.79)
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