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Found 14 papers, 2 papers with code
DK-SLAM: Monocular Visual SLAM with Deep Keypoints Adaptive Learning, Tracking and Loop-Closing
Unreliable feature extraction and matching in handcrafted features undermine the performance of visual SLAM in complex real-world scenarios.
Continual Learning through Networks Splitting and Merging with Dreaming-Meta-Weighted Model Fusion
Differently, we propose a continual learning method named Split2MetaFusion which can achieve better trade-off by employing a two-stage strategy: splitting and meta-weighted fusion.
SuperUDF: Self-supervised UDF Estimation for Surface Reconstruction
The key insight is that if the UDF is estimated correctly, the 3D points should be locally projected onto the underlying surface following the gradient of the UDF.
RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo
In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth.
Learning Task-preferred Inference Routes for Gradient De-conflict in Multi-output DNNs
By designing the learnable task-specific importance variables, DR-MGF evaluates the importance of filters for different tasks.
SOCS: Semantically-aware Object Coordinate Space for Category-Level 6D Object Pose Estimation under Large Shape Variations
We propose Semantically-aware Object Coordinate Space (SOCS) built by warping-and-aligning the objects guided by a sparse set of keypoints with semantically meaningful correspondence.
RayMVSNet: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo
In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth.
3DRM:Pair-wise relation module for 3D object detection
Context has proven to be one of the most important factors in object layout reasoning for 3D scene understanding.
StablePose: Learning 6D Object Poses from Geometrically Stable Patches
According to the theory of geometric stability analysis, a minimal set of three planar/cylindrical patches are geometrically stable and determine the full 6DoFs of the object pose.
SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images
We propose an end-to-end deep neural network which is able to predict both reflectional and rotational symmetries of 3D objects present in the input RGB-D image.
Rescan: Inductive Instance Segmentation for Indoor RGBD Scans
In depth-sensing applications ranging from home robotics to AR/VR, it will be common to acquire 3D scans of interior spaces repeatedly at sparse time intervals (e. g., as part of regular daily use).
PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction
We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction.
Recurrent 3D Attentional Networks for End-to-End Active Object Recognition
Active vision is inherently attention-driven: The agent actively selects views to attend in order to fast achieve the vision task while improving its internal representation of the scene being observed.
