NeRF dynamic scene reconstruction based on motion, semantic information and inpainting
Bibliographic record
Abstract
In this work, we address the inherent limitations of Neural Radiance Field (NeRF) in synthesizing novel viewpoints within dynamic environments, particularly those compromised by moving objects. Such scenarios frequently yield reconstructions of suboptimal quality, characterized by blurriness and the presence of artifacts, which significantly undermines the fidelity of synthetic scenes. This limitation significantly restricts the potential applications of NeRF in autonomous driving contexts, such as scene editing, high-precision map construction, and related functionalities. To overcome these challenges, we propose a novel NeRF-based approach tailored to address the complexities associated with moving objects in monocular driving scenarios. The proposed approach combines optical flow analysis and semantic information to precisely detect and localize moving objects. This was then followed by an inpainting technique that guides the NeRF reconstruction process, effectively mitigating the adverse impacts of dynamic elements within the scene. Our model is further enhanced by incorporating depth and semantic data to refine the training process. We validate the efficacy of our approach through comprehensive experimentation on both synthetic and real-world driving datasets, as well as on challenging self-recorded realistic driving scenes. Our method achieves a performance improvement of up to 13% compared to previous state-of-the-art methods. Additionally, we verify the efficacy of our approach through comprehensive ablation analyses. Both the quantitative and qualitative results demonstrate the superiority especially in dynamic driving scenes, advancing the potential applications in autonomous driving contexts. Our code and self-collected data are available at https://github.com/GandalfTGrey/Nerf-KBS.git .
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".