# Contextual Range-View Projection for 3D LiDAR Point Clouds

Source: [Contextual Range-View Projection for 3D LiDAR Point Clouds](<https://www.sota2.com/research/paper/697846cea2b28e761d87c74d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-13T05:00:04.183Z

Authors: Seyedali Mousavi, Seyedhamidreza Mousavi, Masoud Daneshtalab

Published: 2026-01-26T09:30:43.000Z

## Abstract

Range-view projection provides an efficient method for transforming 3D LiDAR point clouds into 2D range image representations, enabling effective processing with 2D deep learning models. However, a major challenge in this projection is the many-to-one conflict, where multiple 3D points are mapped onto the same pixel in the range image, requiring a selection strategy. Existing approaches typically retain the point with the smallest depth (closest to the LiDAR), disregarding semantic relevance and object structure, which leads to the loss of important contextual information. In this paper, we extend the depth-based selection rule by incorporating contextual information from both instance centers and class labels, introducing two mechanisms: \\textit{Centerness-Aware Projection (CAP)} and \\textit{Class-Weighted-Aware Projection (CWAP)}. In CAP, point depths are adjusted according to their distance from the instance center, thereby prioritizing central instance points over noisy boundary and background points. In CWAP, object classes are prioritized through user-defined weights, offering flexibility in the projection strategy. Our evaluations on the SemanticKITTI dataset show that CAP preserves more instance points during projection, achieving up to a 3.1\\% mIoU improvement compared to the baseline. Furthermore, CWAP enhances the performance of targeted classes while having a negligible impact on the performance of other classes

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on SemanticKITTI (Per-Class IoU and Stuff Metrics)](<https://www.sota2.com/research/sota/semantic-segmentation-on-semantickitti-per-class-iou-and-stuff-metrics.md>): Semantic segmentation; SemanticKITTI; Car IoU: 95.8.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2601.18301>)

[PDF](<https://arxiv.org/pdf/2601.18301.pdf>)