mlsanityData to 14 Oct 2026

PeP: a Point enhanced Painting method for unified point cloud tasks

arXiv 2310.07591 ↗First posted 11 Oct 2023cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

Point encoder is of vital importance for point cloud recognition. As the very beginning step of whole model pipeline, adding features from diverse sources and providing stronger feature encoding mechanism would provide better input for downstream modules. In our work, we proposed a novel PeP module to tackle above issue. PeP contains two main parts, a refined point painting method and a LM-based point encoder. Experiments results on the nuScenes and KITTI datasets validate the superior performance of our PeP. The advantages leads to strong performance on both semantic segmentation and object detection, in both lidar and multi-modal settings. Notably, our PeP module is model agnostic and plug-and-play. Our code will be publicly available soon.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 21 of its 25 references that are in the map. First posted on arXiv 11 Oct 2023.

Builds on

21 of 25 references.

References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  4. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  6. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  9. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  10. 2020Searching Efficient 3D Architectures with Sparse Point-Voxel Convolutioncited by 423
  11. 2023Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Modelscited by 375
  12. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
9 more
  1. 2021RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentationcited by 173
  2. 2021Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Trackingcited by 166
  3. 2021Multimodal Virtual Point 3D Detectioncited by 166
  4. 2019MVX-Net: Multimodal VoxelNet for 3D Object Detectioncited by 163
  5. 2023Spherical Transformer for LiDAR-based 3D Recognitioncited by 125
  6. 2019Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detectioncited by 124
  7. 2023Virtual Sparse Convolution for Multimodal 3D Object Detectioncited by 81
  8. 2016Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networkscited by 72
  9. 2023Position-Guided Point Cloud Panoptic Segmentation Transformercited by 17

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 3.

7 papers.

  1. 2024LVIC: Multi-modality segmentation by Lifting Visual Info as Cuecited by 2
  2. 2024MonoDETRNext: Next-Generation Accurate and Efficient Monocular 3D Object Detectorcited by 1
  3. 2025MonoVQD: Monocular 3D Object Detection with Variational Query Denoising and Self-Distillationcited by 0
  4. 2025Through the Perspective of LiDAR: A Feature-Enriched and Uncertainty-Aware Annotation Pipeline for Terrestrial Point Cloud Segmentationcited by 0
  5. 2026Mono3DV: Monocular 3D Object Detection with 3D-Aware Bipartite Matching and Variational Query DeNoisingcited by 0
  6. 2026SPDA-SAM: A Self-prompted Depth-Aware Segment Anything Model for Instance Segmentationcited by 0
  7. 2026Point Virtual Transformercited by 0