mlsanityData to 14 Oct 2026

Boosting 3D Object Detection via Object-Focused Image Fusion

arXiv 2207.10589 ↗First posted 21 Jul 2022cs.CVcited by 11

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Abstract (arXiv)

3D object detection has achieved remarkable progress by taking point clouds as the only input. However, point clouds often suffer from incomplete geometric structures and the lack of semantic information, which makes detectors hard to accurately classify detected objects. In this work, we focus on how to effectively utilize object-level information from images to boost the performance of point-based 3D detector. We present DeMF, a simple yet effective method to fuse image information into point features. Given a set of point features and image feature maps, DeMF adaptively aggregates image features by taking the projected 2D location of the 3D point as reference. We evaluate our method on the challenging SUN RGB-D dataset, improving state-of-the-art results by a large margin (+2.1 [email protected] and [email protected]). Code is available at https://github.com/haoy945/DeMF.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 36 of its 49 references that are in the map. First posted on arXiv 21 Jul 2022.

Builds on

36 of 49 references.

References · 36

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24 more
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  2. 2018Deformable ConvNets v2: More Deformable, Better Resultscited by 670
  3. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  4. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  5. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  6. 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 410
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  19. 2021FCAF3D: Fully Convolutional Anchor-Free 3D Object Detectioncited by 97
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Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 5; 2024: 1; 2025: 3.

11 papers.

  1. 2023UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representationcited by 74
  2. 2022CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Cloudscited by 58
  3. 2023PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detectioncited by 53
  4. 2023An Object SLAM Framework for Association, Mapping, and High-Level Taskscited by 15
  5. 2023Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrastcited by 14
  6. 2023PRED: Pre-training via Semantic Rendering on LiDAR Point Cloudscited by 7
  7. 2025PointSplit: Towards On-device 3D Object Detection with Heterogeneous Low-power Acceleratorscited by 2
  8. 2024CascadeV-Det: Cascade Point Voting for 3D Object Detectioncited by 1
  9. 2022Review on 6D Object Pose Estimation with the focus on Indoor Scene Understandingcited by 0
  10. 2025Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Drivingcited by 0
  11. 2025GraphFusion3D: Dynamic Graph Attention Convolution with Adaptive Cross-Modal Transformer for 3D Object Detectioncited by 0