Neural Scene Flow Prior
Authors: Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We evaluated the performance (accuracy, generalizability, and computational cost) of our neural prior for scene flow on synthetic and real-world datasets. We performed experiments on different neural network settings and analyzed the performance of the neural prior to regularizing scene flow. Remarkably, we show that a simple MLP-based prior to regularize scene flow is enough to achieve competitive results to the state-of-the-art scene flow methods. |
| Researcher Affiliation | Collaboration | Xueqian Li 1,2 Jhony Kaesemodel Pontes1 Simon Lucey2 1Argo AI 2The University of Adelaide |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | We cite all the data we used in the experiment section. And we will release code in personal Git Hub repository. |
| Open Datasets | Yes | Datasets We used four scene flow datasets: 1) Flying Things3D [33] which is an extensive collection of randomly moving synthetic objects. We used the preprocessed data from [30]; 2) KITTI [34,35] which has real-world self-driving scenes. We used the subset released by [30]; 3) Argoverse [8] and 4) nu Scenes [7] are two large-scale autonomous driving datasets with challenging dynamic scenes. However, there are no official scene flow annotations. We followed the data processing method in [45] to collect pseudo-ground-truth scene flow. |
| Dataset Splits | Yes | Flying Things3D [33] Train: 19,967 samples, Test: 2,000 samples nu Scenes Scene Flow [7] Train: 1,513 samples, Test: 310 samples KITTI Scene Flow [34,35] Train: 100 samples, Test: 50 samples Argoverse Scene Flow [8] Train: 2,691 samples, Test: 212 samples |
| Hardware Specification | Yes | All experiments were run on a machine with an NVIDIA Quadro P5000 GPU and a 16 Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz. |
| Software Dependencies | No | The paper mentions 'Py Torch [41]' and 'Adam [24]' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Implementation details We defined our neural prior for scene flow as a simple coordinate-based MLP architecture with 8 hidden layers, a fixed length of 128 for the hidden units, Rectified Linear Unit (Re LU) activation and shared weights across points. The network input is the 3D point cloud Pt-1, and the output is the scene flow F. We used Py Torch [41] for the implementation and optimized the objective function with Adam [24]. The weights were randomly initialized. We set a fixed learning rate of 8e 3 and run the optimization for 5k iterations with early stopping on the loss. |