DISK: Learning local features with policy gradient
Authors: Michał Tyszkiewicz, Pascal Fua, Eduard Trulls
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on three different benchmarks, and present two ablation studies. |
| Researcher Affiliation | Collaboration | Michał J. Tyszkiewicz1 Pascal Fua1 Eduard Trulls2 1École Polytechnique Fédérale de Lausanne (EPFL) 2Google Research, Zurich michal.tyszkiewicz@epfl.ch pascal.fua@epfl.ch trulls@google.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Training and inference code is available at https://github.com/cvlab-epfl/disk. |
| Open Datasets | Yes | We use a subset of the Mega Depth dataset [19], from which we choose 135 scenes with 63k images in total. |
| Dataset Splits | Yes | We rely on a validation set of two scenes: Sacre Coeur and St. Peter s Square . |
| Hardware Specification | No | The paper mentions "GPU memory" but does not specify any particular GPU model, CPU, or other hardware specifications used for experiments. |
| Software Dependencies | No | The paper mentions using ADAM [17] but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Rewards are λtp = 1, λfp = 0.25 and λkp = 0.001. We use a batch of two scenes, with three images in each. We use ADAM [17] with learning rate of 10 4. To pick the best checkpoint, we evaluate performance in terms of pose estimation accuracy in stereo, with DEGENSAC [7]. Specifically, every 5k optimization steps we compute the mean Average Accuracy (m AA) at a 10o error threshold, as in [16]: see Sec. 4.1 and the appendix for details. We anneal λfp and λkp over the first 5 epochs, starting with 0 and linearly increasing to their full value at the end. Grid cells are square, with each side h = 8 pixels. |