Research map / 3D vision / Depth and stereo
CodeSLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add overlapping frames to the master keyframe optimisation | CodeSLAM · SceneNet RGB-D, master keyframe reconstruction | RMSE [10^-2]: 2.65 -> 2.14 | Helped |
| Use more than 128 code dimensions (ablation) | CodeSLAM depth encoder · SceneNet RGB-D depth reconstruction | reconstruction accuracy | No clear effect |
| Use colour images instead of greyscale images (ablation) | CodeSLAM depth encoder · SceneNet RGB-D depth reconstruction | reconstruction accuracy | No clear effect |
| Use a nonlinear decoder instead of a linear decoder (ablation) | CodeSLAM depth encoder · SceneNet RGB-D depth reconstruction | reconstruction accuracy | No clear effect |
Closest papers by meaning
- IBD-SLAM: Learning Image-Based Depth Fusion for Generalizable SLAM (CVPR 2024 · similarity 0.88)
- MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors (CVPR 2025 · similarity 0.88)
- Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular Stereo and RGB-D Cameras (CVPR 2024 · similarity 0.87)
- Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM (CVPR 2024 · similarity 0.87)
- SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos (CVPR 2025 · similarity 0.86)
- PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustment (CVPR 2024 · similarity 0.86)
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