CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
Authors: Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar, Leonidas J. Guibas
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now evaluate the canonicalization, representation, and reconstruction capabilities of Ca SPR, demonstrate its utility in multiple downstream tasks, and justify design choices. Dataset and Preprocessing: We introduce a new dataset containing simulated rigid motion of objects in three Shape Net [5] categories: cars, chairs, and airplanes. ... Evaluation Procedure: To measure canonicalization errors, T-NOCS coordinates are split into the spatial and temporal part with GT given by X and t respectively. The spatial error at frame k is 1 Mk PMk i=1 bxi xi 2 and the temporal error is 1 Mk PMk i=1 |bti ti| . For reconstruction, the Chamfer Distance (CD) and Earth Mover s Distances (EMD) are measured (and reported multiplied by 103). Lower is better for all metrics; we report the median over all test frames because outlier shapes cause less informative mean errors. |
| Researcher Affiliation | Academia | 1Stanford University 2University of Padova 3ETH Zürich |
| Pseudocode | No | The paper describes the method and architecture in text and diagrams but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a project website URL (geometry.stanford.edu/projects/caspr) but does not explicitly state that the source code for the methodology is available there, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We introduce a new dataset containing simulated rigid motion of objects in three Shape Net [5] categories: cars, chairs, and airplanes. |
| Dataset Splits | No | The paper describes training and testing data usage ('For training, 5 frames...At test time, we use a different...'), but does not explicitly mention or specify details for a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | No | The paper describes the loss functions and data sampling strategy but does not provide concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or system-level training settings in its main text. |