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[ICCV23] Official Implementation of CMDA: Cross-Modality Domain Adaptation for Nighttime Semantic Segmentation
[CVPR2023] CompletionFormer: Depth Completion with Convolutions and Vision Transformers
A novel paradigm for collecting and generating stereo training data using neural rendering
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
[NeurIPS 2023] RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
[CVPR2023] This is an official implementation for "PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes".
[ICCV 2023] Official implementation of "SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance Fields"
Code release for ConvNeXt V2 model
EfficientFormerV2 [ICCV 2023] & EfficientFormer [NeurIPs 2022]
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
Official Implementation of Semantic Image Synthesis via Diffusion Models
[CVPR2023] Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation
This repository contains the source code for our paper: MODE: Multi-view Omnidirectional Depth Estimation with 360-degree Cameras. ECCV 2022
Code for "Deconstructing Monocular Depth Reconstruction: The Design Decisions that Matter" (https://arxiv.org/abs/2208.01489)
[ECCV 2022] Official repository for "MaxViT: Multi-Axis Vision Transformer". SOTA foundation models for classification, detection, segmentation, image quality, and generative modeling...
Label-Efficient Semantic Segmentation with Diffusion Models (ICLR'2022)
Official PyTorch implementation of MonoDEVSNet - "Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision."
🐍 Geometric Computer Vision Library for Spatial AI
[ECCV2022] RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation
[ NeurIPS2021] This is an official implementation of our paper "HRFormer: High-Resolution Transformer for Dense Prediction".
Reproduction of the CVPR 2020 paper - Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume