Fast Kernel Scene Flow

• URL: http://arxiv.org/abs/2403.05896v1
• Date: Sat, 9 Mar 2024 12:24:49 GMT
• Title: Fast Kernel Scene Flow
• Abstract summary: We propose a new positional encoding-based kernel that demonstrates state-of-the-art performance in efficient lidar scene flow estimation. An important highlight of our method is its near real-time performance with dense lidar data (8k-144k points), enabling a variety of practical applications in robotics and autonomous driving scenarios.
• Score: 44.28000710831124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In contrast to current state-of-the-art methods, such as NSFP [25], which employ deep implicit neural functions for modeling scene flow, we present a novel approach that utilizes classical kernel representations. This representation enables our approach to effectively handle dense lidar points while demonstrating exceptional computational efficiency -- compared to recent deep approaches -- achieved through the solution of a linear system. As a runtime optimization-based method, our model exhibits impressive generalizability across various out-of-distribution scenarios, achieving competitive performance on large-scale lidar datasets. We propose a new positional encoding-based kernel that demonstrates state-of-the-art performance in efficient lidar scene flow estimation on large-scale point clouds. An important highlight of our method is its near real-time performance (~150-170 ms) with dense lidar data (~8k-144k points), enabling a variety of practical applications in robotics and autonomous driving scenarios.

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