A novel CAD-aided coarse-to-fine framework of RGBD-to-point clouds registration
Bibliographic record
Abstract
Abstract. Accurate registration between RGB-D images and point clouds is a critical task for various indoor applications. Estimating the relative pose by aligning the sensor frame with indoor 3D point clouds significantly enhances environmental perception and scene understanding. Existing research primarily focuses on cross-modal feature association through traditional unsupervised methods or supervised learning-based approaches. However, these methods often rely on strong assumptions, such as the availability of an initial pose or substantial overlap between the RGB-D images and the target point clouds. Moreover, the quality of registration is highly sensitive to the density and completeness of the point clouds. To address these limitations, this paper presents a novel coarse-to-fine registration framework with the aid of CAD models. First, a data enhancement process is introduced using the Scan2CAD method to replace functional objects (e.g., chairs and tables) with CAD models, improving semantic and quality consistency. Second, a geometry-aware graph matching is computed to identify regions of interest (ROI) within the point cloud map and estimate the initial pose of the RGBD sensor. Finally, an iterative fine matching using cross-modal is introduced to refine the initial estimated pose. Experimental validation on the ScanNet dataset demonstrates that the proposed framework achieves robust and accurate registration between RGB-D images and 3D point clouds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".