Self-supervised monocular depth estimation improves in low-texture regions by using distance transforms on jointly estimated pre-semantic contours to create more informative loss signals.
arXiv preprint arXiv:2002.12319 (2020)
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 2polarities
background 2representative citing papers
DcSConv adapts convolution filter scales based on object depth to reduce size-depth ambiguity in self-supervised monocular depth estimation, improving performance on KITTI by up to 11.6% in SqRel.
SS3D pretrains an end-to-end feed-forward 3D estimator on filtered YouTube-8M videos via SfM self-supervision, MVS filtering, and expert distillation, delivering stronger zero-shot transfer and fine-tuning than prior self-supervised baselines.
NAIMA distills global semantic context from DINOv2 token embeddings into RGB-guided depth super-resolution using cross-attention blocks, reporting gains over prior GDSR methods on multiple datasets and scales.
citing papers explorer
-
Improved monocular depth prediction using distance transform over pre-semantic contours with self-supervised neural networks
Self-supervised monocular depth estimation improves in low-texture regions by using distance transforms on jointly estimated pre-semantic contours to create more informative loss signals.
-
Adaptive Depth-converted-Scale Convolution for Self-supervised Monocular Depth Estimation
DcSConv adapts convolution filter scales based on object depth to reduce size-depth ambiguity in self-supervised monocular depth estimation, improving performance on KITTI by up to 11.6% in SqRel.
-
SS3D: End2End Self-Supervised 3D from Web Videos
SS3D pretrains an end-to-end feed-forward 3D estimator on filtered YouTube-8M videos via SfM self-supervision, MVS filtering, and expert distillation, delivering stronger zero-shot transfer and fine-tuning than prior self-supervised baselines.
-
NAIMA: Semantics Aware RGB Guided Depth Super-Resolution
NAIMA distills global semantic context from DINOv2 token embeddings into RGB-guided depth super-resolution using cross-attention blocks, reporting gains over prior GDSR methods on multiple datasets and scales.