Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Depth Completion with Uncertainty-Driven Loss Functions
Authors: Yufan Zhu, Weisheng Dong, Leida Li, Jinjian Wu, Xin Li, Guangming Shi3626-3634
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method has been tested on KITTI Depth Completion Benchmark and achieved the state-of-the-art robustness performance in terms of MAE, IMAE, and IRMSE metrics. |
| Researcher Affiliation | Academia | 1 School of Artificial Intelligence, Xidian University, Xi an 710071, China 2 Lane Dep. of CSEE, West Virginia University, Morgantown WV 26506, USA |
| Pseudocode | No | The paper describes network architectures and mathematical formulations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The KITTI depth completion benchmark(Uhrig et al. 2017) has 86898 Lidar frames for training, 1000 frames for validation, and 1000 frames for testing. |
| Dataset Splits | Yes | The KITTI depth completion benchmark(Uhrig et al. 2017) has 86898 Lidar frames for training, 1000 frames for validation, and 1000 frames for testing. |
| Hardware Specification | Yes | Our training is implemented by Pytorch with 5 NVIDIA GTX2080Ti GPUs and set batch-size to 5. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | Our training is implemented by Pytorch with 5 NVIDIA GTX2080Ti GPUs and set batch-size to 5. In our current implementation, we have used ADAM (Kingma and Ba 2014) as the optimization algorithm. We have set the learning rate to 1 10 4 when we train our multiscale joint prediction model and 2 10 4 when training uncertainty attention residual learning model. The other parameters are all the same with (β1, β2) = (0.9, 0.999), eps = 1 10 8 and Weight decay = 0. |