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Found 22 papers, 10 papers with code
Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion
The multi-view 2. 5D diffusion directly models the structural distribution of 3D data, while still maintaining the strong generalization ability of the original 2D diffusion model, filling the gap between 2D diffusion-based and direct 3D diffusion-based methods for 3D content generation.
DUA-DA: Distillation-based Unbiased Alignment for Domain Adaptive Object Detection
Though feature-alignment based Domain Adaptive Object Detection (DAOD) have achieved remarkable progress, they ignore the source bias issue, i. e. the aligned features are more favorable towards the source domain, leading to a sub-optimal adaptation.
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling
We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e. g., depth maps).
NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimation
We present a novel differentiable rendering framework for joint geometry, material, and lighting estimation from multi-view images.
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
To this end, we formulate a novel quantization training paradigm to compress the embeddings from the training stage, termed low-precision training (LPT).
Critical Regularizations for Neural Surface Reconstruction in the Wild
The first one is the Hessian regularization that smoothly diffuses the signed distance values to the entire distance field given noisy and incomplete input.
Visibility-aware Multi-view Stereo Network
As such, the adverse influence of occluded pixels is suppressed in the cost fusion.
Ranked #1 on
Point Clouds
on DTU
Learning Stereo Matchability in Disparity Regression Networks
Finally, a matchability-aware disparity refinement is introduced to improve the depth inference in weakly matchable regions.
Ranked #2 on
Stereo Disparity Estimation
on KITTI 2015
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation
In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task.
Ranked #1 on
Video Object Segmentation
on FBMS
Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction
In this work, we propose a stochastic bundle adjustment algorithm which seeks to decompose the RCS approximately inside the LM iterations to improve the efficiency and scalability.
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency
Recent learning-based approaches, in which models are trained by single-view images have shown promising results for monocular 3D face reconstruction, but they suffer from the ill-posed face pose and depth ambiguity issue.
Ranked #6 on
3D Face Reconstruction
on REALY (side-view)
Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts
In this paper, we present a joint multi-task learning framework for semantic segmentation and boundary detection.
ASLFeat: Learning Local Features of Accurate Shape and Localization
This work focuses on mitigating two limitations in the joint learning of local feature detectors and descriptors.
BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
Compared with other computer vision tasks, it is rather difficult to collect a large-scale MVS dataset as it requires expensive active scanners and labor-intensive process to obtain ground truth 3D structures.
Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency
The self-supervised learning of depth and pose from monocular sequences provides an attractive solution by using the photometric consistency of nearby frames as it depends much less on the ground-truth data.
Cross-Atlas Convolution for Parameterization Invariant Learning on Textured Mesh Surface
We present a convolutional network architecture for direct feature learning on mesh surfaces through their atlases of texture maps.
ContextDesc: Local Descriptor Augmentation with Cross-Modality Context
Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations.
Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference
However, one major limitation of current learned MVS approaches is the scalability: the memory-consuming cost volume regularization makes the learned MVS hard to be applied to high-resolution scenes.
Reconstructing Thin Structures of Manifold Surfaces by Integrating Spatial Curves
We present a novel surface reconstruction method using both curves and point clouds.
MVSNet: Depth Inference for Unstructured Multi-view Stereo
We present an end-to-end deep learning architecture for depth map inference from multi-view images.
Ranked #19 on
Point Clouds
on Tanks and Temples
(Mean F1 (Intermediate) metric)
Joint Camera Clustering and Surface Segmentation for Large-Scale Multi-View Stereo
To solve this problem, we propose a joint optimization in a hierarchical framework to obtain the final surface segments and corresponding optimal camera clusters.
Dual-Feature Warping-Based Motion Model Estimation
Line segment is a prominent feature in artificial environments and it can supply sufficient geometrical and structural information of scenes, which not only helps guild to a correct warp in low-texture condition, but also prevents the undesired distortion induced by warping.
