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Found 10 papers, 8 papers with code
Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
Then, another VFM trained on fine-grained 2D masks is adopted to guide the generation of semantically augmented images and point clouds to enhance the performance of neural networks, which mix the data from source and target domains like view frustums (FrustumMixing).
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data.
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Besides, we construct the OpenPCSeg codebase, which is the largest and most comprehensive outdoor LiDAR segmentation codebase.
Ranked #2 on
3D Semantic Segmentation
on SemanticKITTI
(using extra training data)
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Recent advancements in vision foundation models (VFMs) have opened up new possibilities for versatile and efficient visual perception.
Towards Label-free Scene Understanding by Vision Foundation Models
For nuImages and nuScenes datasets, the performance is 22. 1\% and 26. 8\% with improvements of 3. 5\% and 6. 0\%, respectively.
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications.
SCPNet: Semantic Scene Completion on Point Cloud
We propose a simple yet effective label rectification strategy, which uses off-the-shelf panoptic segmentation labels to remove the traces of dynamic objects in completion labels, greatly improving the performance of deep models especially for those moving objects.
Ranked #1 on
3D Semantic Scene Completion
on SemanticKITTI
Rethinking Range View Representation for LiDAR Segmentation
We show that, for the first time, a range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks, i. e., SemanticKITTI, nuScenes, and ScribbleKITTI.
Ranked #4 on
3D Semantic Segmentation
on SemanticKITTI
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion
Notably, LoGoNet ranks 1st on Waymo 3D object detection leaderboard and obtains 81. 02 mAPH (L2) detection performance.
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
For the first time, our pre-trained network achieves annotation-free 3D semantic segmentation with 20. 8% and 25. 08% mIoU on nuScenes and ScanNet, respectively.
