3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds (2304.00690v1)

Abstract: Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) model is largely neglected as most existing benchmarks are dominated by point clouds captured under normal weather. We introduce SemanticSTF, an adverse-weather point cloud dataset that provides dense point-level annotations and allows to study 3DSS under various adverse weather conditions. We study all-weather 3DSS modeling under two setups: 1) domain adaptive 3DSS that adapts from normal-weather data to adverse-weather data; 2) domain generalizable 3DSS that learns all-weather 3DSS models from normal-weather data. Our studies reveal the challenge while existing 3DSS methods encounter adverse-weather data, showing the great value of SemanticSTF in steering the future endeavor along this very meaningful research direction. In addition, we design a domain randomization technique that alternatively randomizes the geometry styles of point clouds and aggregates their embeddings, ultimately leading to a generalizable model that can improve 3DSS under various adverse weather effectively. The SemanticSTF and related codes are available at \url{https://github.com/xiaoaoran/SemanticSTF}.

• The paper presents the SemanticSTF dataset that provides dense, multi-weather LiDAR point cloud annotations across 21 semantic categories.
• It introduces the PointDR technique that uses domain randomization and contrastive learning to enhance model generalization to adverse conditions.
• Extensive experiments demonstrate improved 3D segmentation performance, addressing real-world challenges in autonomous driving under inclement weather.

Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions

The presented paper introduces a comprehensive paper on 3D semantic segmentation (3DSS) of LiDAR point clouds under adverse weather conditions, a vital task for autonomous systems. The authors address an evident gap in research facilitated by the lack of diverse benchmark datasets that capture adverse weather scenarios, which are critical for developing robust autonomous driving systems.

SemanticSTF Dataset

A central contribution of this paper is the introduction of SemanticSTF, a large-scale dataset dedicated to LiDAR point cloud segmentation under adverse weather conditions. Unlike existing datasets that primarily capture normal weather scenarios, SemanticSTF includes dense annotations across 21 semantic categories for data collected in conditions such as fog, snow, and rain. This dataset serves as a crucial resource for advancing research in 3D semantic segmentation across varied and challenging weather conditions.

Domain Adaptation and Generalization

The paper explores two significant aspects of machine learning: domain adaptation and domain generalization. The paper reveals the challenges faced by existing 3DSS methods when exposed to adverse-weather datasets:

1. Domain Adaptive 3DSS: Here, models learn to adapt from normal-weather data to adverse-weather data. This approach recognizes the need for systems to learn and adapt quickly when encountering unseen conditions during deployment.
2. Domain Generalizable 3DSS: Models are trained on normal-weather data with the objective of generalizing performance across various unseen weather conditions. This is particularly challenging given the nominal training data does not include the specific domain changes encountered during testing.

Point Cloud Domain Randomization

The authors propose a novel technique, Point Cloud Domain Randomization (PointDR), which efficiently broadens the training distribution by introducing variations in the geometry of input point clouds through randomization and aggregation of embeddings via contrastive learning. This approach aims to produce a model that generalizes well to adverse-weather conditions without having direct access to such data during training.

Experimental Validation and Results

The paper includes extensive experiments validating the SemanticSTF dataset alongside the efficacy of PointDR against state-of-the-art 3DSS methods such as MinkowskiNet, showing significant improvement in domain adaptation and generalization tasks. While traditional methods focus on object detection with bounding boxes, SemanticSTF provides point-wise annotations crucial for detailed semantic segmentation tasks. The proposed method improves the segmentation performance over challenging weather conditions indicating its potential impact on real-world deployment scenarios.

Implications and Future Directions

This research extends the scope of 3DSS research to include operational challenges faced in varied weather conditions, thereby addressing crucial robustness requirements in autonomous driving. The availability of SemanticSTF will likely spur progress in developing more resilient systems that perform consistently across diverse conditions. Furthermore, the introductions of techniques such as PointDR could inspire analogous methods in other perception tasks beyond LiDAR, potentially influencing the design of comprehensive frameworks for generalizing beyond the training distribution in machine learning.

Overall, this paper provides valuable resources and insights that could shape the future developments of autonomous systems in achieving reliable and robust performance across different environmental conditions. This aligns with a growing demand for scalable models that require minimal adaptation while maintaining accuracy in deployment across untested scenarios.