Search Results for author:
Found 13 papers, 8 papers with code
HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation
In addition, we take advantage of the uncertainty for pose refinement to enhance the performance of APR.
SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching
In this paper, we address the challenge of matching semantically similar keypoints across image pairs.
Cos R-CNN for Online Few-shot Object Detection
We propose Cos R-CNN, a simple exemplar-based R-CNN formulation that is designed for online few-shot object detection.
SimpleRecon: 3D Reconstruction Without 3D Convolutions
Traditionally, 3D indoor scene reconstruction from posed images happens in two phases: per-image depth estimation, followed by depth merging and surface reconstruction.
Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision
Specifically, we demonstrate that regression from vision transformer features without point-level supervision or reference images is superior to other reference-less methods and is competitive with methods that use reference images.
Ranked #11 on
Object Counting
on FSC147
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth
We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available.
Monocular Depth Estimation
Unsupervised Monocular Depth Estimation
Direct-PoseNet: Absolute Pose Regression with Photometric Consistency
We present a relocalization pipeline, which combines an absolute pose regression (APR) network with a novel view synthesis based direct matching module, offering superior accuracy while maintaining low inference time.
Correspondence Networks with Adaptive Neighbourhood Consensus
This is a challenging task due to large intra-class variations and a lack of dense pixel level annotations.
Ranked #11 on
Semantic correspondence
on PF-PASCAL
Domain-invariant Stereo Matching Networks
State-of-the-art stereo matching networks have difficulties in generalizing to new unseen environments due to significant domain differences, such as color, illumination, contrast, and texture.
Thinking Outside the Box: Generation of Unconstrained 3D Room Layouts
We propose a method for room layout estimation that does not rely on the typical box approximation or Manhattan world assumption.
GA-Net: Guided Aggregation Net for End-to-end Stereo Matching
In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities.
Ranked #4 on
Stereo Depth Estimation
on Spring
RelocNet: Continuous Metric Learning Relocalisation using Neural Nets
We propose a method of learning suitable convolutional representations for camera pose retrieval based on nearest neighbour matching and continuous metric learning-based feature descriptors.
Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation
We propose a novel end-to-end trainable, deep, encoder-decoder architecture for single-pass semantic segmentation.
Ranked #7 on
Semantic Segmentation
on CamVid
