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..
Generative View Synthesis: From Single-view Semantics to Novel-view Images
Authors: Tewodros Amberbir Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive experimental analysis on three different multi-view datasets: CARLA [14], Cityscapes [12], and Virtual-KITTI-2 [3]. We show both qualitatively and quantitatively that our approach, which compares favorably with strong baseline techniques, produces novel-view images that are geometrically and semantically consistent. |
| Researcher Affiliation | Collaboration | Tewodros Habtegebrial1,4 Varun Jampani2 Orazio Gallo3 Didier Stricker1,4 1TU Kaiserslautern 2Google Research 3NVIDIA 4DFKI |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | For code and additional results, visit the project page at https://gvsnet.github.io |
| Open Datasets | Yes | We perform experiments on three different datasets: CARLA [14], Virtual-KITTI-2 [3] and Cityscapes [12]. |
| Dataset Splits | No | The paper mentions training networks and evaluation metrics but does not explicitly provide specific training/validation/test split percentages or sample counts for the datasets used. |
| Hardware Specification | Yes | The entire network training does not fit on NVIDIA GTX-2080-Ti GPUs, which is what we use for training. |
| Software Dependencies | No | The paper states: "We implemented our model in Py Torch [26] and use the Adam [22] optimizer for training." While PyTorch is mentioned, a specific version number is not provided, nor are versions for any other software libraries. |
| Experiment Setup | Yes | For our experiments, we used k = 3 lifted semantics layers, m = 32 MPI planes, and f = 20 appearance features per pixel. We implemented our model in Py Torch [26] and use the Adam [22] optimizer for training. In all of our experiments we use images at a resolution of 256 x 256 pixels. We train GVSNet in two stages. In the first stage, we pre-train SUN with the target segmentation and depth losses. In the second stage, we train LTN and ADN with the target color loss, while keeping the SUN fixed. |