3D Reconstruction of Unstructured Objects Using Information From Multiple Sensors
Bibliographic record
Abstract
The Structure-from-Motion (SfM) algorithm is widely used for point cloud reconstruction. However, one drawback of conventional SfM based methods is that the obtained final point sets may contain holes and noise, which could degrade the estimation of reconstructed objects especially for smooth surfaces with few features. The other drawback is the accuracy and speed of SfM based methods depend on the uncertain number of images. To overcome these limitations, this paper proposes a novel 3D reconstruction method for unstructured objects based on the structure from motion in combination with the structured light, in which the point sets of structured light and the point sets of structure from motion can come from different target objects. Since the two point sets coming from multiple sensors do not scale well for register, making it difficult to find corresponding points, a scaled principal component analysis algorithm is proposed for the registration to overcome the impact due to large scale variance. With a large scale factor, a recalculated registration center is proposed via feature region segmentation to achieve point cloud registration again. The two point sets are matched using the proposed optimization method to complete 3D reconstruction. Surface reconstruction is performed using the Poisson algorithm to obtain a smooth surface. The proposed method is tested on some simple structured objects and real-life data of complex unstructured objects collected using range sensors. Compared with several state-of-the-art algorithms, experimental results confirm its potential for surface reconstruction from depth data calculated from the two sets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".