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Found 17 papers, 5 papers with code
SCRREAM : SCan, Register, REnder And Map:A Framework for Annotating Accurate and Dense 3D Indoor Scenes with a Benchmark
However, using these datasets to evaluate dense geometry tasks, such as depth rendering, can be problematic as the meshes of the dataset are often incomplete and may produce wrong ground truth to evaluate the details.
AIM 2024 Sparse Neural Rendering Challenge: Methods and Results
In this challenge, 5 teams submitted final results to Track 1 and 4 teams submitted final results to Track 2.
AIM 2024 Sparse Neural Rendering Challenge: Dataset and Benchmark
A recurring problem in sparse rendering literature is the lack of an homogeneous, up-to-date, dataset and evaluation protocol.
NCRF: Neural Contact Radiance Fields for Free-Viewpoint Rendering of Hand-Object Interaction
Despite remarkable progress that has been achieved in this field, existing methods still fail to synthesize the hand-object interaction photo-realistically, suffering from degraded rendering quality caused by the heavy mutual occlusions between the hand and the object, and inaccurate hand-object pose estimation.
HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
3D head animation has seen major quality and runtime improvements over the last few years, particularly empowered by the advances in differentiable rendering and neural radiance fields.
Human Gaussian Splatting: Real-time Rendering of Animatable Avatars
This work addresses the problem of real-time rendering of photorealistic human body avatars learned from multi-view videos.
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
CLAD: A realistic Continual Learning benchmark for Autonomous Driving
In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Re-examining Distillation For Continual Object Detection
Training models continually to detect and classify objects, from new classes and new domains, remains an open problem.
Model-Based Image Signal Processors via Learnable Dictionaries
Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP).
FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging
We introduce a strategy that learns to jointly align and assess the alignment and exposure reliability using an HDR-aware, uncertainty-driven attention map that robustly merges the frames into a single high quality HDR image.
Residual Contrastive Learning: Unsupervised Representation Learning from Residuals
In the era of deep learning, supervised residual learning (ResL) has led to many breakthroughs in low-level vision such as image restoration and enhancement tasks.
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images
We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs.
NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results
This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021.
Perceptual Video Super Resolution with Enhanced Temporal Consistency
Together with a video discriminator, we also propose additional loss functions to further reinforce temporal consistency in the generated sequences.
