A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation
Authors: Yingda Yin, Yang Wang, He Wang, Baoquan Chen
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments show that our proposed distribution achieves state-of-the-art performance for rotation regression tasks over both probabilistic and non-probabilistic baselines. |
| Researcher Affiliation | Academia | Yingda Yin Yang Wang He Wang Baoquan Chen Peking University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our project page is at pkuepic.github.io/Rotation Laplace. |
| Open Datasets | Yes | Model Net10-SO3 (Liao et al., 2019) is a commonly used synthetic dataset for single image rotation estimation containing 10 object classes. |
| Dataset Splits | No | For Pascal3D+ dataset, it is mentioned that images are split into Image Net train, Image Net val, Pascal VOC train, and Pascal VOC val sets. For Pascal3D+ dataset, we follow Murphy et al. (2021) to use (the more challenging) Pascal VOC val as test set. However, the paper does not provide explicit percentages or counts for the train/validation/test splits used in their experiments. |
| Hardware Specification | Yes | The experiment is done on Model Net10-SO3 toilet dataset on a single 3090 GPU. |
| Software Dependencies | No | The paper mentions using 'pretrained Res Net-101' and 'SGD optimizer' but does not specify software versions for libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | The batch size is set as 32. We use the SGD optimizer and start with the learning rate of 0.01. For Model Net10-SO3 dataset, we train 50 epochs with learning rate decaying by a factor of 10 at epochs 30, 40, and 45. For Pascal3D+ dataset, we train 120 epochs with the same learning rate decay at epochs 30, 60 and 90. |