REVIEW 4 cited by
Self-Supervised Monocular Depth Estimation with Internal Feature Fusion
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Self-Supervised Monocular Depth Estimation with Internal Feature Fusion
read the original abstract
Self-supervised learning for depth estimation uses geometry in image sequences for supervision and shows promising results. Like many computer vision tasks, depth network performance is determined by the capability to learn accurate spatial and semantic representations from images. Therefore, it is natural to exploit semantic segmentation networks for depth estimation. In this work, based on a well-developed semantic segmentation network HRNet, we propose a novel depth estimation network DIFFNet, which can make use of semantic information in down and upsampling procedures. By applying feature fusion and an attention mechanism, our proposed method outperforms the state-of-the-art monocular depth estimation methods on the KITTI benchmark. Our method also demonstrates greater potential on higher resolution training data. We propose an additional extended evaluation strategy by establishing a test set of challenging cases, empirically derived from the standard benchmark.
Forward citations
Cited by 4 Pith papers
-
JEPADepth: Masked Predictive Representation Learning for Self-Supervised Monocular Depth Estimation
Adding an I-JEPA-style masked representation prediction loss to a photometric monocular depth pipeline improves depth accuracy on KITTI and zero-shot transfer to Cityscapes and Make3D.
-
SS3D: End2End Self-Supervised 3D from Web Videos
SS3D pretrains an end-to-end 3D estimator on filtered YouTube-8M videos via SfM self-supervision, achieving improved zero-shot transfer and fine-tuning over prior baselines.
-
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 ...
-
SS3D: End2End Self-Supervised 3D from Web Videos
SS3D scales SfM-based self-supervision to ~100M frames from YouTube-8M using a multi-view signal proxy for filtering and a two-stage training schedule, achieving strong zero-shot transfer and better fine-tuning than p...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.