# HD-OOD3D: Supervised and Unsupervised Out-of-Distribution object detection in LiDAR data

Source: [HD-OOD3D: Supervised and Unsupervised Out-of-Distribution object detection in LiDAR data](<https://www.sota2.com/research/paper/68fa7b451e209131cea876b2>)

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

Data updated: 2025-10-26T05:36:41.717Z

Authors: Louis Soum-Fontez, Jean-Emmanuel Deschaud, Fran\\c{c}ois Goulette

Published: 2024-10-31T09:29:55.000Z

## Abstract

Autonomous systems rely on accurate 3D object detection from LiDAR data, yet most detectors are limited to a predefined set of known classes, making them vulnerable to unexpected out-of-distribution (OOD) objects. In this work, we present HD-OOD3D, a novel two-stage method for detecting unknown objects. We demonstrate the superiority of two-stage approaches over single-stage methods, achieving more robust detection of unknown objects while addressing key challenges in the evaluation protocol. Furthermore, we conduct an in-depth analysis of the standard evaluation protocol for OOD detection, revealing the critical impact of hyperparameter choices. To address the challenge of scaling the learning of unknown objects, we explore unsupervised training strategies to generate pseudo-labels for unknowns. Among the different approaches evaluated, our experiments show that top-5 auto-labelling offers more promising performance compared to simple resizing techniques.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2410.23767>)

[PDF](<https://arxiv.org/pdf/2410.23767.pdf>)