A Simple yet Universal Framework for Depth Completion
Authors: Jin-Hwi Park, Hae-Gon Jeon
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the proposed method s superior generalization capabilities for Uni DC problem over state-of-the-art depth completion. |
| Researcher Affiliation | Academia | Jin-Hwi Park AI Graduate School GIST jinhwipark@gm.gist.ac.kr Hae-Gon Jeon AI Graduate School GIST haegonj@gist.ac.kr |
| Pseudocode | Yes | Algorithm 1 Implementation of Hyperbolic Universal Depth Completion |
| Open Source Code | Yes | Source code is publicly available at https://github.com/Jinhwi Park/Uni DC. |
| Open Datasets | Yes | We employ the NYU Depth V2 dataset, which consists of 464 indoor scenes captured using a Kinect sensor... For outdoor environments, we utilize the KITTI DC dataset, which comprises 90K samples. |
| Dataset Splits | Yes | The dataset is segmented into training (86K samples), validation (7K samples), and testing (1K samples) portions. |
| Hardware Specification | Yes | Our model is implemented with public Py Torch [92], trained on a single RTX 3090Ti GPU using Adam [93] optimizer. |
| Software Dependencies | No | The paper mentions 'public Py Torch [92]' and 'Adam [93] optimizer' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We train our method in a supervised manner with a linear combination of two loss terms... where µ is a balance term and is empirically set to 0.1. ... The initial learning rate was set to 5 × 10^−3 and reduced by 0.1 every 20% for total iterations. |