Papers › 360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation
360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation
Laurenz Reichardt, Nikolas Ebert, Oliver Wasenmüller
Deep learning applications on LiDAR data suffer from a strong domain gap when applied to different sensors or tasks. In order for these methods to obtain similar accuracy on different data in comparison to values reported on public benchmarks, a large scale annotated dataset is necessary. However, in practical applications labeled data is costly and time consuming to obtain. Such factors have triggered various research in label-efficient methods, but a large gap remains to their fully-supervised counterparts. Thus, we propose ImageTo360, an effective and streamlined few-shot approach to label-efficient LiDAR segmentation. Our method utilizes an image teacher network to generate semantic predictions for LiDAR data within a single camera view. The teacher is used to pretrain the LiDAR segmentation student network, prior to optional fine-tuning on 360° data. Our method is implemented in a modular manner on the point level and as such is generalizable to different architectures. We improve over the current state-of-the-art results for label-efficient methods and even surpass some traditional fully-supervised segmentation networks.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Semantic Segmentation | SemanticKITTI | 360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All) | mIOU (1% Test set) | 57.7 | #4 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | SemanticKITTI | 360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All) | mIoU (1% Labels) | 59.5 | #4 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | SemanticKITTI | 360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All) | mIoU (10% Labels) | 62.4 | #4 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | SemanticKITTI | 360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All) | mIoU (20% Labels) | 64.2 | #4 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | SemanticKITTI | 360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All) | mIoU (50% Labels) | 66.1 | #4 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections