Papers Surface Normal Estimation
“Surface Normal Estimation” 태그가 달린 논문 96편 · 필터 해제
Probabilistic Online Event Downsampling
Event cameras capture scene changes asynchronously on a per-pixel basis, enabling extremely high temporal resolution. However, this advantage comes at the cost of high bandwidth, memory, and computational demands. To add…
object-detectionObject DetectionSurface Normal EstimationRobust and Real-time Surface Normal Estimation from Stereo Disparities using Affine Transformations
This work introduces a novel method for surface normal estimation from rectified stereo image pairs, leveraging affine transformations derived from disparity values to achieve fast and accurate results. We demonstrate ho…
GPUSurface Normal EstimationNormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors
Surface normal estimation serves as a cornerstone for a spectrum of computer vision applications. While numerous efforts have been devoted to static image scenarios, ensuring temporal coherence in video-based normal esti…
Surface Normal EstimationLiSu: A Dataset and Method for LiDAR Surface Normal Estimation
While surface normals are widely used to analyse 3D scene geometry, surface normal estimation from LiDAR point clouds remains severely underexplored. This is caused by the lack of large-scale annotated datasets on the on…
Autonomous DrivingDomain AdaptationSurface Normal EstimationSurface ReconstructionImage Gradient-Aided Photometric Stereo Network
Photometric stereo (PS) endeavors to ascertain surface normals using shading clues from photometric images under various illuminations. Recent deep learning-based PS methods often overlook the complexity of object surfac…
regressionSurface Normal EstimationMulti-task Geometric Estimation of Depth and Surface Normal from Monocular 360° Images
Geometric estimation is required for scene understanding and analysis in panoramic 360{\deg} images. Current methods usually predict a single feature, such as depth or surface normal. These methods can lack robustness, e…
Multi-Task LearningScene UnderstandingSurface Normal EstimationFine-Tuning Image-Conditional Diffusion Models is Easier than You Think
Recent work showed that large diffusion models can be reused as highly precise monocular depth estimators by casting depth estimation as an image-conditional image generation task. While the proposed model achieved state…
Conditional Image GenerationDepth EstimationImage GenerationMonocular Depth Estimation+2Sapiens: Foundation for Human Vision Models
We present Sapiens, a family of models for four fundamental human-centric vision tasks -- 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-r…
2D Human Pose Estimation2D Pose EstimationDepth EstimationHuman Part Segmentation+3Elite360M: Efficient 360 Multi-task Learning via Bi-projection Fusion and Cross-task Collaboration
360 cameras capture the entire surrounding environment with a large FoV, exhibiting comprehensive visual information to directly infer the 3D structures, e.g., depth and surface normal, and semantic information simultane…
3D geometryERPMulti-Task LearningSemantic Segmentation+1StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal
This work addresses the challenge of high-quality surface normal estimation from monocular colored inputs (i.e., images and videos), a field which has recently been revolutionized by repurposing diffusion priors. However…
Surface Normal EstimationSurface ReconstructionPanoNormal: Monocular Indoor 360° Surface Normal Estimation
The presence of spherical distortion on the Equirectangular image is an acknowledged challenge in dense regression computer vision tasks, such as surface normal estimation. Recent advances in convolutional neural network…
Surface Normal EstimationEnabling Visual Recognition at Radio Frequency
This paper introduces PanoRadar, a novel RF imaging system that brings RF resolution close to that of LiDAR, while providing resilience against conditions challenging for optical signals. Our LiDAR-comparable 3D imaging …
object-detectionObject DetectionSemantic SegmentationSurface Normal EstimationMetric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation
We introduce Metric3D v2, a geometric foundation model for zero-shot metric depth and surface normal estimation from a single image, which is crucial for metric 3D recovery. While depth and normal are geometrically relat…
Depth EstimationSurface Normal EstimationZero-shot GeneralizationRegion-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning
In this study, we address the intricate challenge of multi-task dense prediction, encompassing tasks such as semantic segmentation, depth estimation, and surface normal estimation, particularly when dealing with partiall…
Depth EstimationSemantic SegmentationSurface Normal EstimationWhat Matters When Repurposing Diffusion Models for General Dense Perception Tasks?
Extensive pre-training with large data is indispensable for downstream geometry and semantic visual perception tasks. Thanks to large-scale text-to-image (T2I) pretraining, recent works show promising results by simply f…
Depth EstimationImage MattingImage SegmentationMonocular Depth Estimation+3Rethinking Inductive Biases for Surface Normal Estimation
Despite the growing demand for accurate surface normal estimation models, existing methods use general-purpose dense prediction models, adopting the same inductive biases as other tasks. In this paper, we discuss the ind…
Surface Normal EstimationSurface Normal Estimation with Transformers
We propose the use of a Transformer to accurately predict normals from point clouds with noise and density variations. Previous learning-based methods utilize PointNet variants to explicitly extract multi-scale features …
Surface Normal EstimationEvent-based Shape from Polarization with Spiking Neural Networks
Recent advances in event-based shape determination from polarization offer a transformative approach that tackles the trade-off between speed and accuracy in capturing surface geometries. In this paper, we investigate ev…
Surface Normal EstimationRFTrans: Leveraging Refractive Flow of Transparent Objects for Surface Normal Estimation and Manipulation
Transparent objects are widely used in our daily lives, making it important to teach robots to interact with them. However, it's not easy because the reflective and refractive effects can make depth cameras fail to give …
global-optimizationSurface Normal EstimationTransparent objectsPolyMaX: General Dense Prediction with Mask Transformer
Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or regression (continuous outputs). This per…
Depth EstimationMonocular Depth EstimationPredictionSemantic Segmentation+2