# Backpropagation-free Network for 3D Test-time Adaptation Source: [Backpropagation-free Network for 3D Test-time Adaptation]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:38:38.445Z Authors: Yanshuo Wang, Ali Cheraghian, Zeeshan Hayder, Jie Hong, Sameera Ramasinghe, Shafin Rahman, David Ahmedt-Aristizabal, Xuesong Li, Lars Petersson, Mehrtash Harandi Published: 2024-03-27T10:50:24.000Z ## Abstract Real-world systems often encounter new data over time, which leads to experiencing target domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally heavy and memory-intensive backpropagation-based approaches to handle this. Here, we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data. Our model uses a two-stream architecture to maintain knowledge about the source domain as well as complementary target-domain-specific information. The backpropagation-free property of our model helps address the well-known forgetting problem and mitigates the error accumulation issue. The proposed method also eliminates the need for the usually noisy process of pseudo-labeling and reliance on costly self-supervised training. Moreover, our method leverages subspace learning, effectively reducing the distribution variance between the two domains. Furthermore, the source-domain-specific and the target-domain-specific streams are aligned using a novel entropy-based adaptive fusion strategy. Extensive experiments on popular benchmarks demonstrate the effectiveness of our method. The code will be available at \\url{https://github.com/abie-e/BFTT3D}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Point Cloud Classification on ShapeNet-C](): 3D Point Cloud Classification; ShapeNet-C; Accuracy (Uniform Noise): 72.58. - [3D Point Cloud Classification on ScanObjectNN-C 1.0 (test)](): 3D Point Cloud Classification; ScanObjectNN-C 1.0 (test); Accuracy (Uniform Noise): 16.35. - [3D Point Cloud Classification on ModelNet40-C (test)](): 3D Point Cloud Classification; ModelNet40-C (test); Robustness to Rotation: 38.17. ## Original sources and implementation [Original paper]() [PDF]()