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..
Revisiting 3D Object Detection From an Egocentric Perspective
Authors: Boyang Deng, Charles R Qi, Mahyar Najibi, Thomas Funkhouser, Yin Zhou, Dragomir Anguelov
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on the large-scale Waymo Open Dataset show that SDE better reflects the impact of detection quality on the ego-agent s safety compared to Io U; and the estimated contours from Star Poly consistently improve the egocentric detection quality over recent 3D object detectors. |
| Researcher Affiliation | Industry | Boyang Deng Charles R. Qi Mahyar Najibi Thomas Funkhouser Yin Zhou Dragomir Anguelov Waymo LLC Google Research |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps in a code-like format. |
| Open Source Code | No | The code and the data are proprietary. We plan to release the code of computing the metrics upon acceptance. |
| Open Datasets | Yes | All analysis is based on the Waymo Open Dataset [53] validation set. |
| Dataset Splits | Yes | All analysis is based on the Waymo Open Dataset [53] validation set. |
| Hardware Specification | Yes | We include this information in the supplementary material for our proposed Star Poly model. |
| Software Dependencies | No | The paper mentions using a 'Point Net [40] model' and 'Adam' optimizer but does not specify version numbers for any software dependencies or libraries required for reproduction. |
| Experiment Setup | Yes | We use a resolution n = 256 for all following experiments. ( d1, ..., dn) is uniformly sampled from the boundary of a square. During training, γ and β are both set to 0.1, which is determined by a grid search over the hyperparameters. Please refer to the supplementary material for more model details. |