Dense Point Cloud Mapping by Leveraging Neural-Based Monocular Depth Estimation
Bibliographic record
Abstract
Accurate and efficient 3D mapping is essential for applications such as robotics, autonomous navigation, and augmented reality. Traditional mapping methods often rely on expensive depth sensors or sparse monocular Simultaneous Localization and Mapping (SLAM) techniques, both of which face limitations in certain environments. This paper proposes a novel pipeline that integrates neural network-based monocular depth estimation (MDE) into an RGB-D SLAM framework, enabling dense 3D mapping using only RGB images. The pipeline utilizes a neural network to infer depth maps from monocular RGB input, followed by a filtering module to ensure depth consistency based on visual odometry. The resulting dense depth maps are used alongside RGB data to generate detailed point clouds. Experimental evaluations conducted in indoor environments demonstrate that the proposed approach significantly enhances the volumetric density and geometric fidelity of 3D point cloud maps compared to traditional RGB-D SLAM systems having near real-time inference rate. The method also addresses key challenges such as sparsity and limited sensor range, while being modular and easy to adapt for other models and subsystems, laying the foundations for robust and cost-effective mapping solutions. Future work will explore extending the framework to outdoor environments and improving real-time performance as well robustness to out-of-distribution scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".