mlsanityData to 14 Oct 2026

TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation

arXiv 2406.01395 ↗First posted 3 Jun 2024cs.CVcited by 1

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Abstract (arXiv)

This paper presents TE-NeXt, a novel and efficient architecture for Traversability Estimation (TE) from sparse LiDAR point clouds based on a residual convolution block. TE-NeXt block fuses notions of current trends such as attention mechanisms and 3D sparse convolutions. TE-NeXt aims to demonstrate high capacity for generalisation in a variety of urban and natural environments, using well-known and accessible datasets such as SemanticKITTI, Rellis-3D and SemanticUSL. Thus, the designed architecture ouperforms state-of-the-art methods in the problem of semantic segmentation, demonstrating better results in unstructured environments and maintaining high reliability and robustness in urbans environments, which leads to better abstraction. Implementation is available in a open repository to the scientific community with the aim of ensuring the reproducibility of results.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 42 of its 77 references that are in the map. First posted on arXiv 3 Jun 2024.

Builds on

42 of 77 references.

References · 42

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30 more
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Citing papers per year: 2025: 1.

1 paper.

  1. 2025MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensitycited by 1