3D-to-2D Distillation for Indoor Scene Parsing
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Abstract (arXiv)
Indoor scene semantic parsing from RGB images is very challenging due to occlusions, object distortion, and viewpoint variations. Going beyond prior works that leverage geometry information, typically paired depth maps, we present a new approach, a 3D-to-2D distillation framework, that enables us to leverage 3D features extracted from large-scale 3D data repository (e.g., ScanNet-v2) to enhance 2D features extracted from RGB images. Our work has three novel contributions. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training, so the 2D network can infer without requiring 3D data. Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration. Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data. Extensive experiments on various datasets, ScanNet-V2, S3DIS, and NYU-v2, demonstrate the superiority of our approach. Also, experimental results show that our 3D-to-2D distillation improves the model generalization.
Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). Builds on 49 of its 67 references that are in the map. First posted on arXiv 6 Apr 2021.
Builds on
49 of 67 references.
References · 49
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Citing papers per year: 2021: 4; 2022: 18; 2023: 8; 2024: 8; 2025: 6; 2026: 3.
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- 2024HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation18 Mar 2024
- 2023Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation12 Dec 2023
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- 2022Towards Efficient 3D Object Detection with Knowledge Distillation30 May 2022
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- 2022Self-Supervised Image Representation Learning with Geometric Set Consistency29 Mar 2022
- 2022DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection17 Mar 2022
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- 2021Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models8 Jun 2021
- 2021Pri3D: Can 3D Priors Help 2D Representation Learning?22 Apr 2021
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- 20222DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Cloudscited by 157
- 2022Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoderscited by 143
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- 2021Pri3D: Can 3D Priors Help 2D Representation Learning?cited by 61
- 2021Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Modelscited by 41
- 2023EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understandingcited by 32
- 2022Towards Efficient 3D Object Detection with Knowledge Distillationcited by 29
- 2023X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detectioncited by 28
- 2022A Closer Look at Invariances in Self-supervised Pre-training for 3D Visioncited by 24
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12 most cited of 44 All 44 (JSON)
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- 20263D Reconstruction and Knowledge Distillation to Improve Multi-View Image Models to Explore Spike Volume Estimation in Wheat20 May 2026
- 2025CasPoinTr: Point Cloud Completion with Cascaded Networks and Knowledge Distillation27 Sep 2025
- 2025EgoAdapt: Adaptive Multisensory Distillation and Policy Learning for Efficient Egocentric Perception26 Jun 2025
- 20252D-3D Attention and Entropy for Pose Robust 2D Facial Recognition14 May 2025
- 2025GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects21 Mar 2025
- 2025Project-and-Fuse: Improving RGB-D Semantic Segmentation via Graph Convolution Networks31 Jan 2025
- 20243DPX: Single Panoramic X-ray Analysis Guided by 3D Oral Structure Reconstruction27 Sep 2024
- 2024Formula-Supervised Visual-Geometric Pre-training20 Sep 2024
- 2024DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos3 Sep 2024
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12 newest of 44 All 44 (JSON)