Reproduce “NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation” on OpenResearch

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About this paper

Zehan Zheng, Danni Wu, Ruisi Lu, Fan Lu, Guang Chen, Changjun Jiang

NeuralPCI uses neighboring LiDAR point clouds to fit a continuous motion representation across space and time, then queries it for moments between captures. On NL-Drive, a motion-focused stress test, its average Chamfer Distance (CD) is 0.80 across three equally spaced interpolated frames, versus 1.06 for PointINet. This supports lower CD on this benchmark, not broad real-world generalization.

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