Probabilistic Orientation Estimation with Matrix Fisher Distributions
Authors: David Mohlin, Josephine Sullivan, Gérald Bianchi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our proposed approach on three separate datasets Pascal3D+, Model Net10-SO(3) and UPNA head pose. Table 1 compares the performance of our method and recent high performing approaches. |
| Researcher Affiliation | Collaboration | David Mohlin KTH/Tobii Stockholm, Sweden davmo@kth.se Gérald Bianchi Tobii Danderyd, Sweden gerald.bianchi@tobii.com Josephine Sullivan KTH Stockholm, Sweden sullivan@kth.se |
| Pseudocode | No | The paper describes the approach mathematically and textually but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Davmo049/Public_prob_orientation_estimation_with_matrix _fisher_distributions |
| Open Datasets | Yes | Pascal3D+ [27] has 12 rigid object classes and contains images from Pascal VOC and Image Net of these classes. Model Net10-SO(3)[12] is a synthetic dataset. UPNA head pose [1] consists of videos with synchronized annotations of keypoints for the face in the image as well as its 3D rotation and position. |
| Dataset Splits | Yes | We have done all development and hyper-parameter optimization where the full training set was partitioned into a training and validation set. For the Pascal3D+ dataset, we use the Image Net validation split for the test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper mentions 'pytorch s implementation of SVD' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We fine-tune the embedding and fully connected layer weights for 2 epochs. We use SGD and start with a learning rate of 0.01. We use a batch size of 32 and train for 120 epochs. For Pascal3D+ we reduce this learning rate by a factor 10 at epochs 30, 60 and 90. For Model Net10-SO(3) we train for 50 epochs and reduce the learning rate by a factor of 10 at epochs 30, 40 and 45. For UPNA head pose we use the same hyperparameters as for Pascal3D+, except we do not use a class embedding since there are only faces in this dataset. |