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..
Point Cloud Completion with Pretrained Text-to-Image Diffusion Models
Authors: Yoni Kasten, Ohad Rahamim, Gal Chechik
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate SDS-Complete on a collection of incomplete scanned objects, captured by real-world depth sensors and Li DAR scanners. We find that it effectively reconstructs objects that are absent from common datasets, reducing Chamfer loss by about 50% on average compared with current methods. |
| Researcher Affiliation | Collaboration | Yoni Kasten1 Ohad Rahamim2 Gal Chechik1,2 1NVIDIA Research 2Bar-Ilan University |
| Pseudocode | No | No clearly labeled pseudocode or algorithm block was found in the paper. |
| Open Source Code | Yes | Project page: https://sds-complete.github.io/ |
| Open Datasets | Yes | For depth images, we used the Redwood dataset [12] that contains a diverse set of objects. ... We further tested our model on the KITTI Li DAR dataset [7, 17], which contains incomplete point clouds of objects in real-world scenes captured by Li DAR sensors. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset split percentages or sample counts for their experiments. |
| Hardware Specification | Yes | All times are measured when running our method on NVIDIA RTX A6000. |
| Software Dependencies | Yes | As a text-to-image diffusion model we use Stable Diffusion v2 [43]. |
| Experiment Setup | Yes | We optimize the networks using the Adam optimizer [26] with a learning rate 10 4. The coefficients for our loss for all the experiments are δm = 105, δd = 105, δp = 105, δeikonal = 104, δplane = 105. At each iteration we sample 1000 uniform points for Lplane and Leikonal. |