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..
3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction
Authors: Jongmin Lee, Minsu Cho
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method achieves state-of-the-art performance on standard single object pose estimation benchmarks, including Model Net10-SO(3) and PASCAL3D+, demonstrating high sampling efficiency and strong generalization to unseen 3D rotations. |
| Researcher Affiliation | Collaboration | Pohang University of Science and Technology (POSTECH), South Korea EMAIL *The current affiliation of Jongmin Lee is with LG AI Research. Contact: EMAIL. |
| Pseudocode | No | The paper does not contain a pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | No | We will also make our code publicly available after acceptance. |
| Open Datasets | Yes | Model Net10-SO(3) [42] is a common dataset for estimating a 3D rotation from a single image. ... PASCAL3D+ [68] is a widely-used benchmark for evaluating pose estimation in images captured in real-world settings. |
| Dataset Splits | Yes | We use a maximum frequency level of L = 6, resulting in a total size of M = 455, computed as P6 l=0(2l + 1) (2l + 1). ...At inference, we employ a recursive level 5 of SO(3) HEALPix grid with 2.36 million points, achieving a precision of 1.875 , as in [28, 35]. |
| Hardware Specification | Yes | We use a machine with an Intel i7-8700 CPU and an NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | We utilize the e3nn library [22] for S2 and SO(3) convolutions for efficient handling of both Fourier and inverse Fourier transforms, healpy [24, 81] for HEALPix grid generation, and Py Torch [53] for model implementation. |
| Experiment Setup | Yes | With a batch size of 64, our network is trained for 50 epochs on Model Net10-SO(3) taking 25 hours, and for 80 epochs on PASCAL3D+ taking 28 hours. We start with an initial learning rate of 0.1, which decays by a factor of 0.1 every 30 epochs. We use the SGD optimizer with Nesterov momentum set at 0.9. |