Compact Depth-Wise Separable Precise Network for Depth Completion
Bibliographic record
Abstract
Predicting a dense depth map from synchronized LiDAR scans and RGB images using compact deep neural networks presents a significant challenge. While most state-of-the-art models enhance prediction accuracy by increasing the number of parameters, leading to substantial memory consumption, depth completion tasks in areas such as autonomous driving primarily utilize edge devices powered by embedded GPUs. In this paper, we introduce a methodology for creating an efficient, high-fidelity depth completion model derived from a base model. Our proposed compact model replaces conventional convolutional encoder layers with depth-wise separable convolutions, and transposed convolutional decoders with up-sampling plus depth-wise separable convolution. We further employ random layer pruning as a stability test, guiding the design of our architecture and preventing over-parameterization. Additionally, we introduce a straightforward yet robust knowledge distillation method to enhance network performance and improve model scalability to meet higher quality requirements. Our experimental results demonstrate substantial improvement over existing compact models in terms of state-of-the-art performance, while significantly reducing the number of parameters compared to larger models.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".