VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis
Authors: Angtian Wang, Peng Wang, Jian Sun, Adam Kortylewski, Alan Yuille
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative and qualitative experiment results show Vo GE outperforms So TA counterparts when applied to various vision tasks, e.g., object pose estimation, shape/texture fitting, and occlusion reasoning. |
| Researcher Affiliation | Collaboration | Angtian Wang1, Peng Wang2, Jian Sun2, Adam Kortylewski3, and Alan Yuille1 1 Johns Hopkins University 2Byte Dance Inc. 3Max Planck Institute for Informatics |
| Pseudocode | No | No pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | Yes | The code is available: https://github.com/Angtian/Vo GE. |
| Open Datasets | Yes | Dataset. Following Ne Mo, we evaluate pose estimation performance on the PASCAL3D+ dataset Xiang et al. (2014), the Occluded PASCAL3D+ dataset Wang et al. (2020b) and the Object Net3D dataset Xiang et al. (2016). |
| Dataset Splits | No | The PASCAL3D+ dataset contains objects in 12 man-made categories with 11045 training images and 10812 testing images. |
| Hardware Specification | No | Using CUDA from NVIDIA et al. (2022), we implement Vo GE with both forward and backward function. The CUDA-Vo GE is packed as an easy-to-use autogradable Py Torch API. |
| Software Dependencies | Yes | Using CUDA from NVIDIA et al. (2022), we implement Vo GE with both forward and backward function. The CUDA-Vo GE is packed as an easy-to-use autogradable Py Torch API. NVIDIA, P eter Vingelmann, and Frank H.P. Fitzek. Cuda, release: 11.2.89, 2022. URL https: //developer.nvidia.com/cuda-toolkit. |
| Experiment Setup | Yes | Experiment Details. Following the experiment setup in Ne Mo, we train the feature extractor 800 epochs with a progressive learning rate. During inference, for each image, we sample 144 starting poses and optimizer 300 steps via an ADAM optimizer. |