Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |