Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation
Keonhee Han, Dominik Muhle, Felix Wimbauer, Daniel Cremers
Abstract
direct supervision b) knowledge distillation a) self-supervised scene reconstruction multi-view scene reconstruction single-view scene reconstruction rendering density color sampled points density field Figure 1. Knowledge Distillation from Multi-View to Single-View. We propose to boost single-view scene completion by exploiting additional information from multiple images. a) we first train a novel multi-view scene reconstruction algorithm that is able to fuse density fields from multiple images in a fully self-supervised manner. b) we then employ knowledge distillation to directly supervise a state-ofthe-art single-view reconstruction model in 3D to boost its performance.
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Cited by top-tier papers3
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- IPFormer: Visual 3D Panoptic Scene Completion with Context-Adaptive Instance ProposalsMarkus Gross, Aya Fahmy, Danit Niwattananan, Dominik Muhle et al.NeurIPS 2025 · 3 citations
- Feed-Forward SceneDINO for Unsupervised Semantic Scene CompletionAleksandar Jevtic, Christoph Reich, Felix Wimbauer, Oliver Hahn et al.ICCV 2025 · 3 citations
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- Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance FieldsDor Verbin, Peter Hedman, Ben Mildenhall, Todd E. Zickler et al.CVPR 2022 · 477 citations
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- HR-Depth: High Resolution Self-Supervised Monocular Depth EstimationXiaoyang Lyu, Liang Liu, Mengmeng Wang, Xin Kong et al.AAAI 2021 · 341 citations
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