Research map / 3D vision / Depth and stereo
CNN-SLAM: Real-Time Dense Monocular SLAM With Learned Depth Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Fuse CNN-predicted depth with direct monocular SLAM depth refinement | CNN-SLAM · ICL-NUIM and TUM RGB-D SLAM (Avg.) | Absolute Trajectory Error [m]: 0.512 -> 0.246 | Helped |
| Fuse CNN-predicted depth with direct monocular SLAM depth refinement | CNN-SLAM · ICL-NUIM and TUM RGB-D SLAM (Avg.) | Perc. Correct Depth: 18.452 -> 22.464 | Helped |
| Adjust CNN depth using the current-to-training camera focal-length ratio (ablation) | CNN-SLAM · ICL-NUIM sequence shown in Figure | depth accuracy | Helped |
| Refine focal-length-adjusted CNN depth with small-baseline stereo matching (ablation) | CNN-SLAM · ICL-NUIM sequence shown in Figure | depth accuracy | Helped |
Closest papers by meaning
- IBD-SLAM: Learning Image-Based Depth Fusion for Generalizable SLAM (CVPR 2024 · similarity 0.90)
- Depth2Pose: A Pose-Based Benchmark for Monocular Depth Estimation without Ground-Truth Depth (NeurIPS 2026 · similarity 0.88)
- SNI-SLAM: Semantic Neural Implicit SLAM (CVPR 2024 · similarity 0.87)
- MegaSaM: Accurate, Fast and Robust Structure and Motion from Casual Dynamic Videos (CVPR 2025 · similarity 0.87)
- SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos (CVPR 2025 · similarity 0.86)
- DERD-Net: Learning Depth from Event-based Ray Densities (NeurIPS 2025 · similarity 0.86)
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