Volumetric Correspondence Networks for Optical Flow
Authors: Gengshan Yang, Deva Ramanan
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our innovations dramatically improve accuracy over SOTA on standard benchmarks while being significantly easier to work with training converges in 7X fewer iterations |
| Researcher Affiliation | Collaboration | Gengshan Yang1 , Deva Ramanan1,2 1Carnegie Mellon University, 2Argo AI {gengshay, deva}@cs.cmu.edu |
| Pseudocode | No | Not found. The paper describes methods and components but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code will be available at github.com/gengshay-y/VCN. |
| Open Datasets | Yes | Number of training iterations is recorded for the pre-training stage on Flying Chairs and Flying Things, and (S) indicates sequential training on separate modules. |
| Dataset Splits | No | Number of training iterations is recorded for the pre-training stage on Flying Chairs and Flying Things, and (S) indicates sequential training on separate modules. As shown in Tab. 2, after the pretraining stage, ours-small achieves smaller end-point-error (EPE) than all methods on KITTI [9, 30] |
| Hardware Specification | Yes | The model is trained on a machine with 4 Titan X Pascal GPUs. |
| Software Dependencies | No | We build the model and re-implement the training pipeline of PWC-Net+ [39] using Pytorch. |
| Experiment Setup | Yes | We find correspondences with 9 9 search windows on a feature pyramid with stride {64, 32, 16, 8, 4}. We keep K = {16, 16, 16, 16, 12} hypotheses at each scale. To be noted, we are able to stably train the network with a larger learning rate (10 3 vs 10 4) and fewer iterations (140K vs 1200K on Flying Chairs and 80K vs 500K on Flying Things) compared to prior optical flow networks. |