Papers RGB-D Reconstruction
“RGB-D Reconstruction” 태그가 달린 논문 14편 · 필터 해제
Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging
Passive, compact, single-shot 3D sensing is useful in many application areas such as microscopy, medical imaging, surgical navigation, and autonomous driving where form factor, time, and power constraints can exist. Obta…
Autonomous DrivingDeblurringDepth EstimationDisparity Estimation+1LiveNVS: Neural View Synthesis on Live RGB-D Streams
Existing real-time RGB-D reconstruction approaches, like Kinect Fusion, lack real-time photo-realistic visualization. This is due to noisy, oversmoothed or incomplete geometry and blurry textures which are fused from imp…
Neural RenderingNovel View SynthesisRGB-D ReconstructionMIPS-Fusion: Multi-Implicit-Submaps for Scalable and Robust Online Neural RGB-D Reconstruction
We introduce MIPS-Fusion, a robust and scalable online RGB-D reconstruction method based on a novel neural implicit representation -- multi-implicit-submap. Different from existing neural RGB-D reconstruction methods lac…
RGB-D ReconstructionHigh-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 EstimationNeural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera
We propose Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera. In NDR, we adopt the neural implicit function for surf…
Dynamic Reconstructionglobal-optimizationMonocular ReconstructionRGB-D Reconstruction+1INS-Conv: Incremental Sparse Convolution for Online 3D Segmentation
We propose INS-Conv, an INcremental Sparse Convolutional network which enables online accurate 3D semantic and instance segmentation. Benefiting from the incremental nature of RGB-D reconstruction, we only need to up…
CPUGPUInstance SegmentationRGB-D Reconstruction+2Depth-supervised NeRF: Fewer Views and Faster Training for Free
A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enfor…
NeRFRGB-D ReconstructionJoint Texture and Geometry Optimization for RGB-D Reconstruction
Due to inevitable noises and quantization error, the reconstructed 3D models via RGB-D sensors always accompany geometric error and camera drifting, which consequently lead to blurring and unnatural texture mapping resul…
3D ReconstructionQuantizationRGB-D ReconstructionDeepDeform: Learning Non-Rigid RGB-D Reconstruction With Semi-Supervised Data
Applying data-driven approaches to non-rigid 3D reconstruction has been difficult, which we believe can be attributed to the lack of a large-scale training corpus. Unfortunately, this method fails for important cases suc…
3D ReconstructionRGB-D ReconstructionFusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation
Online semantic 3D segmentation in company with real-time RGB-D reconstruction poses special challenges such as how to perform 3D convolution directly over the progressively fused 3D geometric data, and how to smartly fu…
RGB-D ReconstructionScene SegmentationDeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data
Applying data-driven approaches to non-rigid 3D reconstruction has been difficult, which we believe can be attributed to the lack of a large-scale training corpus. Unfortunately, this method fails for important cases suc…
3D ReconstructionRGB-D ReconstructionPlane-Based Optimization of Geometry and Texture for RGB-D Reconstruction of Indoor Scenes
We present a novel approach to reconstruct RGB-D indoor scene with plane primitives. Our approach takes as input a RGB-D sequence and a dense coarse mesh reconstructed by some 3D reconstruction method on the sequence, an…
3D ReconstructionRGB-D ReconstructionEfficient Plane-Based Optimization of Geometry and Texture for Indoor RGB-D Reconstruction
We propose a novel approach to reconstruct RGB-D indoor scene based on plane primitives. Our approach takes as input a RGB-D sequence and a dense coarse mesh reconstructed from it, and generates a lightweight, low-polygo…
RGB-D ReconstructionPlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction
We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction. The core of our method is a deep convolutional neural net that takes in RGB, depth, and normal information of a…
PredictionRGB-D Reconstruction