Papers Surface Normal Estimation
“Surface Normal Estimation” 태그가 달린 논문 96편 · 필터 해제
TSP-Transformer: Task-Specific Prompts Boosted Transformer for Holistic Scene Understanding
Holistic scene understanding includes semantic segmentation, surface normal estimation, object boundary detection, depth estimation, etc. The key aspect of this problem is to learn representation effectively, as each sub…
Boundary DetectionDepth EstimationRepresentation LearningScene Understanding+3Decodable and Sample Invariant Continuous Object Encoder
We propose Hyper-Dimensional Function Encoding (HDFE). Given samples of a continuous object (e.g. a function), HDFE produces an explicit vector representation of the given object, invariant to the sample distribution and…
ObjectSurface Normal EstimationFOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic Data
Surface reconstruction from multi-view images is a challenging task, with solutions often requiring a large number of sampled images with high overlap. We seek to develop a method for few-view reconstruction, for the cas…
Outlier DetectionSurface Normal EstimationSurface ReconstructionCLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement
Contrastive language image pretraining (CLIP) is a standard method for training vision-language models. While CLIP is scalable, promptable, and robust to distribution shifts on image classification tasks, it lacks object…
Depth Estimationimage-classificationImage ClassificationObject Localization+3Large-scale Monocular Depth Estimation in the Wild
Estimating the relative depth of a single image (Monocular Depth Estimation) is a significant step towards understanding the general structure of the depicted scenery, the relations of entities in the scene and their int…
Depth EstimationDepth PredictionMonocular Depth EstimationSurface Normal Estimation+1Prompt Guided Transformer for Multi-Task Dense Prediction
Task-conditional architecture offers advantage in parameter efficiency but falls short in performance compared to state-of-the-art multi-decoder methods. How to trade off performance and model parameters is an important …
Boundary DetectionDecoderMonocular Depth EstimationMulti-Task Learning+3TransNet: Transparent Object Manipulation Through Category-Level Pose Estimation
Transparent objects present multiple distinct challenges to visual perception systems. First, their lack of distinguishing visual features makes transparent objects harder to detect and localize than opaque objects. Even…
Depth CompletionObjectPose EstimationSurface Normal Estimation+1Independent Component Alignment for Multi-Task Learning
In a multi-task learning (MTL) setting, a single model is trained to tackle a diverse set of tasks jointly. Despite rapid progress in the field, MTL remains challenging due to optimization issues such as conflicting and …
Depth EstimationInstance SegmentationMulti-Task LearningSemantic Segmentation+1D2NT: A High-Performing Depth-to-Normal Translator
Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information. However, the state-of-the-art (SoTA) surface normal estimators (SNEs) generally suffer fr…
Surface Normal EstimationVocal Bursts Intensity PredictionPointersect: Neural Rendering with Cloud-Ray Intersection
We propose a novel method that renders point clouds as if they are surfaces. The proposed method is differentiable and requires no scene-specific optimization. This unique capability enables, out-of-the-box, surface norm…
Inverse RenderingNeural RenderingSurface Normal EstimationSurface ReconstructioniDisc: Internal Discretization for Monocular Depth Estimation
Monocular depth estimation is fundamental for 3D scene understanding and downstream applications. However, even under the supervised setup, it is still challenging and ill-posed due to the lack of full geometric constrai…
Autonomous DrivingDepth EstimationMonocular Depth EstimationScene Understanding+2Event Fusion Photometric Stereo Network
We present a novel method to estimate the surface normal of an object in an ambient light environment using RGB and event cameras. Modern photometric stereo methods rely on an RGB camera, mainly in a dark room, to avoid …
Surface Normal EstimationA Large Scale Homography Benchmark
We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D. The applications of…
Homography EstimationSurface Normal EstimationA Large-Scale Homography Benchmark
We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D. The application…
Homography EstimationSurface Normal EstimationMulti-Task Learning with Knowledge Distillation for Dense Prediction
While multi-task learning (MTL) has become an attractive topic, its training usually poses more difficulties than the single-task case. How to successfully apply knowledge distillation into MTL to improve training ef…
Boundary DetectionDepth EstimationKnowledge DistillationMulti-Task Learning+2CORE: Co-planarity Regularized Monocular Geometry Estimation with Weak Supervision
The ill-posed nature of monocular 3D geometry (depth map and surface normals) estimation makes it rely mostly on data-driven approaches such as Deep Neural Networks (DNN). However, data acquisition of surface normals…
3D geometryDepth EstimationMulti-Task LearningSurface Normal Estimation+1DiLiGenT-Pi: Photometric Stereo for Planar Surfaces with Rich Details - Benchmark Dataset and Beyond
Photometric stereo aims to recover detailed surface shapes from images captured under varying illuminations. However, existing real-world datasets primarily focus on evaluating photometric stereo for general non-Lamb…
3D ReconstructionSurface Normal EstimationReLeaPS : Reinforcement Learning-based Illumination Planning for Generalized Photometric Stereo
Illumination planning in photometric stereo aims to find a balance between tween surface normal estimation accuracy and image capturing efficiency by selecting optimal light configurations. It depends on factors such…
reinforcement-learningReinforcement LearningSurface Normal EstimationFast Globally Optimal Surface Normal Estimation from an Affine Correspondence
We present a new solver for estimating a surface normal from a single affine correspondence in two calibrated views. The proposed approach provides a new globally optimal solution for this over-determined problem and…
Surface Normal EstimationVisual LocalizationHigh-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF
Fine-detailed reconstructions are in high demand in many applications. However, most of the existing RGB-D reconstruction methods rely on pre-calculated accurate camera poses to recover the detailed surface geometry, whe…
RGB-D ReconstructionSurface Normal Estimation