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 [1].
Robust Inverse Graphics via Probabilistic Inference
Authors: Tuan Anh Le, Pavel Sountsov, Matthew Douglas Hoffman, Ben Lee, Brian Patton, Rif A. Saurous
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the RIG approach on 3D datasets with a number of prior and Ne RF representations, across a number of possible corruptions. We empirically show that full probabilistic inference produces better results than point estimates on the monocular depth |
| Researcher Affiliation | Industry | 1Google. Correspondence to: Tuan Anh Le <EMAIL>, Pavel Sountsov <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Reconstruction-guidance diffusion conditioning with auxiliary latents (Re GAL) |
| Open Source Code | Yes | The source code for many of these experiments is available at https://github. com/tensorflow/probability/tree/main/ discussion/robust_inverse_graphics. |
| Open Datasets | Yes | Datasets We evaluate our method on two datasets. For the ο¬rst dataset, we use the cars category from Shape Net (Chang et al., 2015). For the second dataset, we use the Multi Shape Net (MSN) dataset (Sajjadi et al., 2022). |
| Dataset Splits | Yes | The dataset consists of 3486 cars, where 3137 are used for training and the remaining 349 for evaluation. |
| Hardware Specification | Yes | On a single A100 GPU, for each image it takes 9.5 minutes for Multi Shape Net and 7.5 minutes for Shape Net to run Re GAL for 2000 steps to generate 8 particles. |
| Software Dependencies | No | The paper mentions using Adam optimizer and refers to Real NVP, but does not provide specific version numbers for any software dependencies or libraries like TensorFlow, PyTorch, or Python versions. |
| Experiment Setup | Yes | Prob Ne RF We train for 2 * 10^6 steps. We use the Adam (Kingma & Ba, 2017) optimizer with a learning rate schedule where we warm up the learning rate from 0 to 10^-4 over 50 steps, and then step-wise halve it every 50000 steps afterward. We used a minibatch of 8 scenes. For the guide, we use 10 random views per scene. SSDNe RF We train for 5 * 10^5 steps. For Shape Net, we use the Adam optimizer with a learning rate schedule where we warm up the learning rate from 0 to 10^-3 and then step-wise halve it every 125000 steps afterward. |