Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
Authors: Shengjie Zhu, Abhinav Kumar, Masa Hu, Xiaoming Liu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the effectiveness of our method through superior performance on synthetic and zero-shot testing datasets. Beyond calibration, we demonstrate downstream applications in image manipulation detection & restoration, uncalibrated two-view pose estimation, and 3D sensing. Codes/models are held here. 4 Experiments Implementation Details Our network is trained using the Adam optimizer [32] with a batch size of 8. The learning rate is 1e 5, and the training process runs for 20, 000 steps. Our RANSAC algorithm is executed on a GPU, performing Kr = 2, 048 iterations, with each iteration including a random sample of Kc = 20, 000 incidence vectors. |
| Researcher Affiliation | Academia | Shengjie Zhu, Abhinav Kumar, Masa Hu, and Xiaoming Liu Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824 {zhusheng, kumarab6, huynshen}@msu.edu, liuxm@cse.msu.edu |
| Pseudocode | No | The paper does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper states "Codes/models are held here." in the abstract, but does not provide a concrete link or explicit statement that the code for the described method is publicly released. |
| Open Datasets | Yes | In Tab. 2, we incorporate datasets of different application scenarios, including indoor, outdoor scenes, driving, and object-centric scenes. Many of the datasets utilize only a single type of camera for data collection, resulting in a scarcity of intrinsic variations. Similar to [38], we employ random resizing and cropping to synthesize more intrinsic, marked in Tab. 2 column Syn.. ... Nu Scenes [10] Calibrated Driving ... KITTI [22] Calibrated Driving ... Cityscapes [14] Calibrated Driving ... NYUv2 [53] Calibrated Indoor ... ARKit Scenes [8] Calibrated Indoor ... SUN3D [65] Calibrated Indoor ... MVImg Net [68] Sf M Object ... Objectron [1] Sf M Object ... Mega Depth [41] Sf M Outdoor ... Waymo [58] Calibrated Driving ... RGBD [55] Pre-defined Indoor ... Scan Net [16] Calibrated Indoor ... MVS [21] Pre-defined Indoor ... Scenes11 [11] Pre-defined Synthetic |
| Dataset Splits | No | The paper mentions training and testing, and augmentation strategies, but does not provide specific details on validation dataset splits (e.g., percentages, sample counts, or explicit validation sets). |
| Hardware Specification | Yes | Tested on an RTX-2080 Ti GPU, the combined network inference and calibration algorithm runs on average in 87 ms per image. |
| Software Dependencies | No | The paper mentions "Our network is trained using the Adam optimizer [32]" but does not specify versions for other software dependencies like programming languages or libraries (e.g., Python, PyTorch). |
| Experiment Setup | Yes | Implementation Details Our network is trained using the Adam optimizer [32] with a batch size of 8. The learning rate is 1e 5, and the training process runs for 20, 000 steps. Our RANSAC algorithm is executed on a GPU, performing Kr = 2, 048 iterations, with each iteration including a random sample of Kc = 20, 000 incidence vectors. During both training and testing, the image is resized to a resolution of 480 640. The threshold for determining RANSAC inliers is kx = ky = 0.02. |