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..
Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
Authors: Minhyuk Sung, Hao Su, Ronald Yu, Leonidas J. Guibas
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our technique in various segmentation and keypoint selection applications. In experiments, we test our model with existing neural network architectures, and demonstrate the performance on labeled/unlabeled segmentation and keypoint correspondence problem in various datasets. |
| Researcher Affiliation | Academia | Minhyuk Sung Stanford University EMAIL Hao Su University of California San Diego EMAIL Ronald Yu University of California San Diego EMAIL Leonidas Guibas Stanford University EMAIL |
| Pseudocode | Yes | 1: function SINGLE STEP GRADIENT ITERATION(X, f, Θt, η) 2: Compute: At = A(X; Θt). 3: Solve: xt = arg minx Atx f 2 2 s.t. C(x). 4: Update: Θt+1 = Θt η L(A(X; Θt); f, xt). 5: end function Algorithm 1: Single-Step Gradient Iteration. |
| Open Source Code | Yes | Code for all experiments below is available in https://github.com/mhsung/deep-functional-dictionaries. |
| Open Datasets | Yes | Yi et al. [41] provide keypoint annotations on 6,243 chair models in Shape Net [7]. Stanford 3D Indoor Semantic Dataset (S3DIS) [2] is a collection of real scan data of indoor scenes with annotations of instance segments and their semantic labels. Here, we test with 100 non-rigid human body shapes in MPI-FAUST dataset [4]. |
| Dataset Splits | Yes | We follow their experiment setup by using the same split of training/validation/test sets and the same 2k sampled point cloud as inputs. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Point Net [32] architecture but does not specify software versions for libraries, frameworks, or operating systems. |
| Experiment Setup | Yes | In the experiment, we use a 80-20 random split for training/test sets 1, train the network with 2k point clouds as provided by [41], and set k = 10 and γ = 0.0. Table 1 shows the results of our method when using k = 10 and γ = 1.0. Thus, we use k = 150 and γ = 1.0. σ is Gaussian-weighting parameter (0.001 in our experiment). |