BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth Estimation
In the domain of multi-baseline stereo, the conventional understanding is that, in general, increasing baseline separation substantially enhances the accuracy of depth estimation. However, prevailing self-supervised depth estimation architectures primarily use minimal frame separation and a constrained stereo baseline. Larger frame separations can be employed; however, we show this to result in diminished depth quality due to various factors, including significant changes in brightness, and increased areas of occlusion. In response to these challenges, our proposed method, BaseBoostDepth, incorporates a curriculum learning-inspired optimization strategy to effectively leverage larger frame separations. However, we show that our curriculum learning-inspired strategy alone does not suffice, as larger baselines still cause pose estimation drifts. Therefore, we introduce incremental pose estimation to enhance the accuracy of pose estimations, resulting in significant improvements across all depth metrics. Additionally, to improve the robustness of the model, we introduce error-induced reconstructions, which optimize reconstructions with added error to the pose estimations. Ultimately, our final depth network achieves state-of-the-art performance on KITTI and SYNS-patches datasets across image-based, edge-based, and point cloud-based metrics without increasing computational complexity at test time. The project website can be found at https://kieran514.github.io/BaseBoostDepth-Project.
Code (1)
Tasks
Depth EstimationMonocular Depth EstimationPose EstimationSimilar Papers 제목 키워드 기반
KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation
Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning. Nevertheless, in facilitating semi-supervised contr…
DiversityLanguage ModelingLanguage ModellingText GenerationSEED: Self-supervised Distillation For Visual Representation
This paper is concerned with self-supervised learning for small models. The problem is motivated by our empirical studies that while the widely used contrastive self-supervised learning method has shown great progress on…
Knowledge DistillationSelf-Supervised LearningUnsupervised Pre-trainingSPLIT: Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography
Machine learning has achieved impressive performance in tomographic reconstruction, but supervised training requires paired measurements and ground-truth images that are often unavailable. This has motivated self-supervi…
Masked Autoencoders are Scalable Learners of Cellular Morphology
Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research. Prior results have shown that deep vision models can c…
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation
Deep neural networks are commonly used for automated medical image segmentation, but models will frequently struggle to generalize well across different imaging modalities. This issue is particularly problematic due to t…
Brain Tumor SegmentationImage SegmentationImage-to-Image TranslationMedical Image Segmentation+4