Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Volume Rendering of Neural Implicit Surfaces
Authors: Lior Yariv, Jiatao Gu, Yoni Kasten, Yaron Lipman
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,203 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,512 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on the challenging task of multiview 3D surface reconstruction. We use two datasets: DTU [12] and Blended MVS [37], both containing real objects with different materials that are captured from multiple views. In Section 4.1 we show qualitative and quantitative 3D surface reconstruction results of Vol SDF, comparing favorably to relevant baselines. |
| Researcher Affiliation | Collaboration | Lior Yariv1 Jiatao Gu2 Yoni Kasten1 Yaron Lipman1,2 1Weizmann Institute of Science 2Facebook AI Research |
| Pseudocode | Yes | Algorithm 1: Sampling algorithm. Input: error threshold ϵ > 0; β 1 Initialize T = T0 2 Initialize β+ such that BT ,β+ ϵ 3 while BT ,β > ϵ and not max_iter do 4 upsample T 5 if BT ,β+ < ϵ then 6 Find β (β, β+) so that BT ,β = ϵ 7 Update β+ β 10 Estimate b O using T and β+ 11 S get fresh m samples using ˆO 1 12 return S |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | We use two datasets: DTU [12] and Blended MVS [37], both containing real objects with different materials that are captured from multiple views. |
| Dataset Splits | No | The paper mentions training on a collection of images and evaluating on specific scans from the DTU and Blended MVS datasets, but it does not explicitly provide details about the training, validation, and test data splits (e.g., percentages, sample counts, or references to predefined splits within this paper). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or cloud computing instances). |
| Software Dependencies | No | The paper does not provide specific details about software dependencies, such as programming languages, libraries, or frameworks, along with their version numbers. |
| Experiment Setup | Yes | We train with batches of size 1024 pixels p. λ is a hyper-parameter set to 0.1 throughout the the experiments. |