2 citations · 6 across the 14 of their papers we have counts for
15 papers
SILICA: Repurposing Diffusion Priors for Joint Glass Segmentation and Depth Estimation
Tarun R, Anuj Verma, Laksh Nanwani +2
Standard depth sensors systematically fail on transparent surfaces, creating corrupted 3D maps and severe navigation hazards. While specialized hardware sensors can detect glass, t…
PixelLoop: Shortcut Topological Navigation with Pixel-Level Loops
Sarthak Chittawar, Vansh Garg, Aditya Vadali +4
Although topological mapping and navigation have been studied extensively, the specific role and downstream effect of loop closures in purely topological representations has receiv…
MASt3R-Nav: WayPixel Navigation in Relative 3D Maps
Vansh Garg, Rohit Jayanti, Krish Pandya +5
Visual navigation ability is strongly tied to its underlying representation of the world. Unlike classical 3D maps that require globally-consistent geometry, image- or object-relat…
SceneEdited: A City-Scale Benchmark for 3D HD Map Updating via Image-Guided Change Detection
Chun-Jung Lin, Tat-Jun Chin, Sourav Garg +1
Accurate, up-to-date High-Definition (HD) maps are critical for urban planning, infrastructure monitoring, and autonomous navigation. However, these maps quickly become outdated as…
SegMASt3R: Geometry Grounded Segment Matching
Rohit Jayanti, Swayam Agrawal, Vansh Garg +4
Segment matching is an important intermediate task in computer vision that establishes correspondences between semantically or geometrically coherent regions across images. Unlike…
ObjectReact: Learning Object-Relative Control for Visual Navigation
Sourav Garg, Dustin Craggs, Vineeth Bhat +5
Visual navigation using only a single camera and a topological map has recently become an appealing alternative to methods that require additional sensors and 3D maps. This is typi…
TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals
Stefan Podgorski, Sourav Garg, Mehdi Hosseinzadeh +3
Visual navigation in robotics traditionally relies on globally-consistent 3D maps or learned controllers, which can be computationally expensive and difficult to generalize across…
To Ask or Not to Ask? Detecting Absence of Information in Vision and Language Navigation
Savitha Sam Abraham, Sourav Garg, Feras Dayoub
Recent research in Vision Language Navigation (VLN) has overlooked the development of agents' inquisitive abilities, which allow them to ask clarifying questions when instructions…
Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames
Krishan Rana, Jad Abou-Chakra, Sourav Garg +3
Imitation learning has unlocked the potential for robots to exhibit highly dexterous behaviours. However, it still struggles with long-horizon, multi-object tasks due to poor sampl…
Revisit Anything: Visual Place Recognition via Image Segment Retrieval
Kartik Garg, Sai Shubodh Puligilla, Shishir Kolathaya +2
Accurately recognizing a revisited place is crucial for embodied agents to localize and navigate. This requires visual representations to be distinct, despite strong variations in…
Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms
Chun-Jung Lin, Sourav Garg, Tat-Jun Chin +1
We present a novel method for scene change detection that leverages the robust feature extraction capabilities of a visual foundational model, DINOv2, and integrates full-image cro…
RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation
Sourav Garg, Krishan Rana, Mehdi Hosseinzadeh +4
Mapping is crucial for spatial reasoning, planning and robot navigation. Existing approaches range from metric, which require precise geometry-based optimization, to purely topolog…
QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding
Yash Mehan, Kumaraditya Gupta, Rohit Jayanti +3
Robotic tasks such as planning and navigation require a hierarchical semantic understanding of a scene, which could include multiple floors and rooms. Current methods primarily foc…
Hierarchical Unsupervised Topological SLAM
Ayush Sharma, Yash Mehan, Pradyumna Dasu +2
In this paper we present a novel framework for unsupervised topological clustering resulting in improved loop. In this paper we present a novel framework for unsupervised topologic…
DiffPrompter: Differentiable Implicit Visual Prompts for Semantic-Segmentation in Adverse Conditions
Sanket Kalwar, Mihir Ungarala, Shruti Jain +4
Semantic segmentation in adverse weather scenarios is a critical task for autonomous driving systems. While foundation models have shown promise, the need for specialized adaptors…