Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering

• URL: http://arxiv.org/abs/2401.06704v2
• Date: Wed, 7 Feb 2024 17:26:53 GMT
• Title: Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering
• Abstract summary: We introduce a highly efficient method for panoptic segmentation of large 3D point clouds. Our model is over $30$ times smaller than the best-competing method and trains up to $15$ times faster.
• Score: 7.680851067579922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a highly efficient method for panoptic segmentation of large 3D point clouds by redefining this task as a scalable graph clustering problem. This approach can be trained using only local auxiliary tasks, thereby eliminating the resource-intensive instance-matching step during training. Moreover, our formulation can easily be adapted to the superpoint paradigm, further increasing its efficiency. This allows our model to process scenes with millions of points and thousands of objects in a single inference. Our method, called SuperCluster, achieves a new state-of-the-art panoptic segmentation performance for two indoor scanning datasets: $50.1$ PQ ($+7.8$) for S3DIS Area~5, and $58.7$ PQ ($+25.2$) for ScanNetV2. We also set the first state-of-the-art for two large-scale mobile mapping benchmarks: KITTI-360 and DALES. With only $209$k parameters, our model is over $30$ times smaller than the best-competing method and trains up to $15$ times faster. Our code and pretrained models are available at https://github.com/drprojects/superpoint_transformer.

Related papers

• EZ-SP: Fast and Lightweight Superpoint-Based 3D Segmentation [14.136068396056869]
  We propose a learnable, fully GPU partitioning algorithm that generates geometrically and semantically coherent superpoints 13$times$ faster than prior methods.<n>Our module is compact (under 60k parameters), trains in under 20 minutes with a differentiable surrogate loss, and requires no handcrafted features.<n>With 72$times$ faster inference and 120$times$ fewer parameters, EZ-SP matches the accuracy of point-based SOTA models across three domains.
  arXiv  Detail & Related papers  (2025-11-29T08:21:51Z)
• PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models [51.24979014650188]
  We present PointSeg, a training-free paradigm that leverages off-the-shelf vision foundation models to address 3D scene perception tasks. PointSeg can segment anything in 3D scene by acquiring accurate 3D prompts to align their corresponding pixels across frames. Our approach significantly surpasses the state-of-the-art specialist training-free model by 14.1$%$, 12.3$%$, and 12.6$%$ mAP on ScanNet, ScanNet++, and KITTI-360 datasets.
  arXiv  Detail & Related papers  (2024-03-11T03:28:20Z)
• Efficient 3D Semantic Segmentation with Superpoint Transformer [7.680851067579922]
  We introduce a novel superpoint-based transformer architecture for efficient semantic segmentation of large-scale 3D scenes. Our method incorporates a fast algorithm to partition point clouds into a hierarchical superpoint structure, which makes our preprocessing 7 times faster than existing approaches. Our model can be trained on a single GPU in 3 hours for a fold of the S3DIS dataset, which is 7x to 70x fewer GPU-hours than the best-performing methods.
  arXiv  Detail & Related papers  (2023-06-13T18:03:05Z)
• Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion [110.84357383258818]
  We propose a novel approach to lift 2D segments to 3D and fuse them by means of a neural field representation. The core of our approach is a slow-fast clustering objective function, which is scalable and well-suited for scenes with a large number of objects. Our approach outperforms the state-of-the-art on challenging scenes from the ScanNet, Hypersim, and Replica datasets.
  arXiv  Detail & Related papers  (2023-06-07T17:57:45Z)
• ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution [14.88505076974645]
  ISBNet is a novel method that represents instances as kernels and decodes instance masks via dynamic convolution. We set new state-of-the-art results on ScanNetV2 (55.9), S3DIS (60.8), S3LS3D (49.2) in terms of AP and retains fast inference time (237ms per scene on ScanNetV2.
  arXiv  Detail & Related papers  (2023-03-01T06:06:28Z)
• Stratified Transformer for 3D Point Cloud Segmentation [89.9698499437732]
  Stratified Transformer is able to capture long-range contexts and demonstrates strong generalization ability and high performance. To combat the challenges posed by irregular point arrangements, we propose first-layer point embedding to aggregate local information. Experiments demonstrate the effectiveness and superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets.
  arXiv  Detail & Related papers  (2022-03-28T05:35:16Z)
• Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks [64.27814530457042]
  We propose an end-to-end solution of Semantic Superpoint Tree Network (SSTNet) for proposing object instances from scene points. Key in SSTNet is an intermediate, semantic superpoint tree (SST), which is constructed based on the learned semantic features of superpoints. SSTNet ranks top on the ScanNet (V2) leaderboard, with 2% higher of mAP than the second best method.
  arXiv  Detail & Related papers  (2021-08-17T07:25:14Z)
• Hierarchical Aggregation for 3D Instance Segmentation [41.20244892803604]
  We propose a clustering-based framework named HAIS, which makes full use of spatial relation of points and point sets. It ranks 1st on the ScanNet v2 benchmark, achieving the highest 69.9% AP50 and surpassing previous state-of-the-art (SOTA) methods by a large margin.
  arXiv  Detail & Related papers  (2021-08-05T03:34:34Z)
• Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [52.464516118826765]
  We introduce RandLA-Net, an efficient and lightweight neural architecture to infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches.
  arXiv  Detail & Related papers  (2021-07-06T05:08:34Z)
• SALA: Soft Assignment Local Aggregation for Parameter Efficient 3D Semantic Segmentation [65.96170587706148]
  We focus on designing a point local aggregation function that yields parameter efficient networks for 3D point cloud semantic segmentation. We explore the idea of using learnable neighbor-to-grid soft assignment in grid-based aggregation functions.
  arXiv  Detail & Related papers  (2020-12-29T20:16:37Z)
• Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU [87.48110331544885]
  We propose a novel training methodology to train and scale the existing semantic segmentation models. We demonstrate a clear benefit of our approach on a dataset with 1284 classes, bootstrapped from LVIS and COCO annotations, with three times better mIoU than the DeeplabV3+ model.
  arXiv  Detail & Related papers  (2020-12-14T13:12:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.