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Found 14 papers, 3 papers with code
Learning A Zero-shot Occupancy Network from Vision Foundation Models via Self-supervised Adaptation
Thus, we adapt the relative depth derived from VFMs into metric depth by optimising the scale and offset using temporal consistency, also known as novel view synthesis, without access to ground-truth metric depth.
CPAny: Couple With Any Encoder to Refer Multi-Object Tracking
Referring Multi-Object Tracking (RMOT) aims to localize target trajectories specified by natural language expressions in videos.
Self-Supervised Multi-Frame Neural Scene Flow
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving.
SurroundSDF: Implicit 3D Scene Understanding Based on Signed Distance Field
Vision-centric 3D environment understanding is both vital and challenging for autonomous driving systems.
Globally Optimal Event-Based Divergence Estimation for Ventral Landing
This is achieved by estimating divergence (inverse TTC), which is the rate of radial optic flow, from the event stream generated during landing.
Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation
As a structured prediction task, scene graph generation, given an input image, aims to explicitly model objects and their relationships by constructing a visually-grounded scene graph.
Importance Weighted Structure Learning for Scene Graph Generation
Scene graph generation is a structured prediction task aiming to explicitly model objects and their relationships via constructing a visually-grounded scene graph for an input image.
Asynchronous Optimisation for Event-based Visual Odometry
Event cameras open up new possibilities for robotic perception due to their low latency and high dynamic range.
Constrained Structure Learning for Scene Graph Generation
As a structured prediction task, scene graph generation aims to build a visually-grounded scene graph to explicitly model objects and their relationships in an input image.
Neural Belief Propagation for Scene Graph Generation
Scene graph generation aims to interpret an input image by explicitly modelling the potential objects and their relationships, which is predominantly solved by the message passing neural network models in previous methods.
Spatiotemporal Registration for Event-based Visual Odometry
The state-of-the-art method of contrast maximisation recovers the motion from a batch of events by maximising the contrast of the image of warped events.
Topological Sweep for Multi-Target Detection of Geostationary Space Objects
In this paper, we propose a novel multi-target detection technique based on topological sweep, to find GEO objects from a short sequence of optical images.
Globally Optimal Contrast Maximisation for Event-based Motion Estimation
To alleviate this weakness, we propose a new globally optimal event-based motion estimation algorithm.
Visual Semantic Information Pursuit: A Survey
Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual semantic information pursuit methods.
