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..
Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance
Authors: Congyue Deng, Jiahui Lei, William B Shen, Kostas Daniilidis, Leonidas J. Guibas
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments conducted on both articulated objects and multi-object scans, we demonstrate the efficacy of our approach in achieving strong generalization under inter-part transformations, even when confronted with substantial changes in pointcloud geometry and topology. and 5 Experiments |
| Researcher Affiliation | Academia | Congyue Deng1 Jiahui Lei2 Bokui Shen1 Kostas Daniilidis2,3 Leonidas Guibas1 1 Stanford University 2 University of Pennsylvania 3 Archimedes, Athena RC EMAIL, EMAIL |
| Pseudocode | No | The paper describes the network architecture and message passing verbally and with diagrams (Figure 3), but does not provide a formally labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We begin by evaluating our method on articulated-object part segmentation using on four categories of the Shape2Motion dataset [74]: washing machine, oven, eyeglasses, and refrigerator. and Dyn Lab [30] is a collection of scanned laboratory scenes, each with 2-3 rigidly moving solid objects captured under 8 different configurations with object positions randomly changed. |
| Dataset Splits | No | The paper describes training on 'single rest state' and testing on 'articulated states' for Shape2Motion, and overfitting to the 'first configuration' for Dyn Lab, but does not provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or citations to predefined splits with such details). |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using a 'SE(3)-equivariant framework called Vector Neurons (VN) [15]' but does not provide specific version numbers for any software dependencies or libraries used in the implementation or experiments. |
| Experiment Setup | Yes | To demonstrate the generalizability of our approach, we train our model on objects in a single rest state, such as ovens with closed doors (as depicted in Fig. 5, left). This training setup aligns with many synthetic datasets featuring static shapes [9, 52]. and For weight truncation [72], we use the lā-Lipschitz as it is the loosest constraint and set the per-layer Lipschitz upper bound to be 0.99999. For regularization losses, we set the overall network Lipschitz threshold to 0.99. The adversarial sampling [69] loss only works with l2-norm. We reduce the total number of points from 2048 to 1024 and the GNN neighbors from 40 to 20 due to the large memory consumption for the loss computations. |