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..
Towards Learning Group-Equivariant Features for Domain Adaptive 3D Detection
Authors: Sangyun Shin, Yuhang He, Madhu Vankadari, Ta-Ying Cheng, Qian Xie, Andrew Markham, Niki Trigoni
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive adaptation experiments on KITTI, Waymo, and Nu Scenes datasets show the effectiveness of our approach for bridging inter-domain gaps. |
| Researcher Affiliation | Academia | Department of Computer Science, University of Oxford, United Kingdom School of Computer Science, University of Leeds, United Kingdom |
| Pseudocode | Yes | Algorithm 1 Training Pipeline |
| Open Source Code | Yes | https://github.com/yunshin/Group Exp-DA.git |
| Open Datasets | Yes | We evaluate our methods against various baselines across three different datasets, such as KITTI [6], Nu Scenes [1], and Waymo [33]. |
| Dataset Splits | Yes | KITTI contains 7481 frames of point clouds for training and validation, and all the data is collected with 64-beam Velodyne Li DAR. Nu Scenes dataset contains 28130 training and 6019 validation point clouds collected with a 32-beam roof Li DAR. Waymo dataset contains 122000 training and 30407 validation frames of point clouds collected with five Li DAR sensors, i.e., one 64-beam Li DAR and four 200-beam Li DAR. |
| Hardware Specification | Yes | Following [17], we first train each detector for 50 epochs with batch-size 8 as a pretraining step using a single NVIDIA A10 GPU. |
| Software Dependencies | No | The paper mentions 'Open PCDet [35]' as the implementation basis but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For a fair comparison with existing domain adaptive 3D detection methods, we build our model on two base detectors, Second Io U [42] and Point Pillars [12], following [44, 43, 17, 8], that are widely used and applicable to most recent detectors with the implementation based on Open PCDet [35] and parameters from ST3D [44]. Following [17], we first train each detector for 50 epochs with batch-size 8 as a pretraining step using a single NVIDIA A10 GPU. In the self-training stage, we train 30 more epochs for the tuning to adapt to the target domain. The learning rate is set to 1 15 4 using Adam optimizer with Cosine annealing [47] for scheduling the learning rate. ... The feature dimensions dbev and dobj for Fbev and µ are both set to 512 for c Attn. α for updating the group parameters is empirically set to 0.8. λ1, λ2, and λ3 are set to 0.5, 0.5, and 1.0, respectively. |