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Repurposing diffusion-based image generators for monocular depth estimation
Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth
from a single image is geometrically ill-posed and requires scene understanding so it is not …
from a single image is geometrically ill-posed and requires scene understanding so it is not …
Learning to upsample by learning to sample
We present DySample, an ultra-lightweight and effective dynamic upsampler. While
impressive performance gains have been witnessed from recent kernel-based dynamic …
impressive performance gains have been witnessed from recent kernel-based dynamic …
Bevdepth: Acquisition of reliable depth for multi-view 3d object detection
In this research, we propose a new 3D object detector with a trustworthy depth estimation,
dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work …
dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work …
Ddp: Diffusion model for dense visual prediction
We propose a simple, efficient, yet powerful framework for dense visual predictions based
on the conditional diffusion pipeline. Our approach follows a" noise-to-map" generative …
on the conditional diffusion pipeline. Our approach follows a" noise-to-map" generative …
Datasetdm: Synthesizing data with perception annotations using diffusion models
Current deep networks are very data-hungry and benefit from training on large-scale
datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data …
datasets, which are often time-consuming to collect and annotate. By contrast, synthetic data …
Detrs with hybrid matching
One-to-one set matching is a key design for DETR to establish its end-to-end capability, so
that object detection does not require a hand-crafted NMS (non-maximum suppression) to …
that object detection does not require a hand-crafted NMS (non-maximum suppression) to …
Monovit: Self-supervised monocular depth estimation with a vision transformer
Self-supervised monocular depth estimation is an attractive solution that does not require
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …
Binsformer: Revisiting adaptive bins for monocular depth estimation
Monocular depth estimation (MDE) is a fundamental task in computer vision and has drawn
increasing attention. Recently, some methods reformulate it as a classification-regression …
increasing attention. Recently, some methods reformulate it as a classification-regression …
Completionformer: Depth completion with convolutions and vision transformers
Given sparse depths and the corresponding RGB images, depth completion aims at spatially
propagating the sparse measurements throughout the whole image to get a dense depth …
propagating the sparse measurements throughout the whole image to get a dense depth …
Robodepth: Robust out-of-distribution depth estimation under corruptions
Depth estimation from monocular images is pivotal for real-world visual perception systems.
While current learning-based depth estimation models train and test on meticulously curated …
While current learning-based depth estimation models train and test on meticulously curated …