E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning
Authors: Xiuhong Lin, Changjie Qiu, zhipeng cai, Siqi Shen, Yu Zang, Weiquan Liu, Xuesheng Bian, Matthias Müller, Cheng Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the MVSEC and VECtor datasets demonstrate the superiority of E2PNet over hand-crafted and other learning-based methods. |
| Researcher Affiliation | Collaboration | a Fujian Key Lab of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University (XMU), China. b Key Laboratory of Multimedia Trusted Perception and Efficient Computing, XMU, China. c Intel Labs. d Apple Inc. e Yancheng Institute Of Technology, China. |
| Pseudocode | No | The paper describes its methods in detail through text and diagrams (Figure 2, 3) but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The abstract states 'The source code can be found at: E2PNet.', but it does not provide a direct link (URL) to a repository or explicitly state that it is in supplementary materials. |
| Open Datasets | Yes | We use the widely used MVSEC [43] and VECtor [44] to build the MVSEC-E2P and VECtor-E2P datasets, which incorporate Li DAR, traditional cameras and event cameras at the same time. |
| Dataset Splits | Yes | MVSEC [43] uses a 16-beam Li DAR and an event camera with a resolution of (346,260)... we use the indoor-x and indoor-y sequences for training and testing respectively, where x [1, 3] and y = 4. VECtor [44] uses a 128-beam Li DAR and an event camera with a resolution of (640,480)... We use the units-dolly, units-scooter, corridors-dolly and corridors-walk sequences for training, and the school-dolly and school-scooter sequences for evaluation. |
| Hardware Specification | Yes | Training is done following the setup of individual baselines on a 3090Ti GPU. All experiments were performed with a batch size of 1 on a 3090Ti GPU using the MVSEC-E2P dataset. |
| Software Dependencies | No | All methods are implemented using Pytorch. However, the paper does not specify the version number for Pytorch or any other software dependencies. |
| Experiment Setup | Yes | Implementation Details. We follow the FEN [26, 20] principle and acquire 20000 consecutive events at a time and sample N = 8192 events from them... All methods are implemented using Pytorch. Training is done following the setup of individual baselines on a 3090Ti GPU... All experiments were performed with a batch size of 1 on a 3090Ti GPU using the MVSEC-E2P dataset. |