arXiv subjects

Suman Ghosh

Publications and source records attributed to Suman Ghosh.

At least 19 recordsLinked to original sources

arXiv2026

DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs

Consumer LiDARs in mobile devices and robots typically output a single depth value per pixel. Yet internally, they record full time-resolved histograms containing direct and multi-bounce light returns; these multi-bounce returns encode rich non-line-of-sight (NLOS) cues that can enable perception of hidden objects in a scene. However, severe hardware limitations of consumer LiDARs make NLOS reconstruction with conventional methods difficult. In this work, we motivate a complementary direction: enabling NLOS perception with low-cost LiDARs through data-driven inference. We present DENALI, the first large-scale real-world dataset of space-time histograms from low-cost LiDARs capturing hidden objects. We capture time-resolved LiDAR histograms for 72,000 hidden-object scenes across diverse object shapes, positions, lighting conditions, and spatial resolutions. Using our dataset, we show that consumer LiDARs can enable accurate, data-driven NLOS perception. We further identify key scene and modeling factors that limit performance, as well as simulation-fidelity gaps that hinder current sim-to-real transfer, motivating future work toward scalable NLOS vision with consumer LiDARs.

cs.RO↗

arXiv2026

SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery

Engineered image-based biomarkers offer a clinically interpretable alternative to black-box AI in computational pathology, yet their discovery remains largely intuition-driven, guided by fragmented literature rather than rigorous biological validation. We introduce SAGE (Structured Agentic system for hypothesis Generation and Evaluation), a multi-agent framework that grounds biomarker discovery in biological evidence through three mechanisms: (i) knowledge-graph-anchored hypothesis generation via multi-path ontological reasoning, (ii) a debate-based multi-agent novelty assessment that stress-tests candidate biomarkers against existing literature, and (iii) an end-to-end automated validation pipeline that translates hypotheses directly into executable analyses on multimodal pathology datasets. Together, these components shift biomarker discovery from an intuition-driven, literature-browsing exercise into a structured, traceable reasoning process that clinicians and researchers can inspect, trust, and build upon.

cs.LG↗

arXiv2025

DERD-Net: Learning Depth from Event-based Ray Densities

Event cameras offer a promising avenue for multi-view stereo depth estimation and Simultaneous Localization And Mapping (SLAM) due to their ability to detect blur-free 3D edges at high-speed and over broad illumination conditions. However, traditional deep learning frameworks designed for conventional cameras struggle with the asynchronous, stream-like nature of event data, as their architectures are optimized for discrete, image-like inputs. We propose a scalable, flexible and adaptable framework for pixel-wise depth estimation with event cameras in both monocular and stereo setups. The 3D scene structure is encoded into disparity space images (DSIs), representing spatial densities of rays obtained by back-projecting events into space via known camera poses. Our neural network processes local subregions of the DSIs combining 3D convolutions and a recurrent structure to recognize valuable patterns for depth prediction. Local processing enables fast inference with full parallelization and ensures constant ultra-low model complexity and memory costs, regardless of camera resolution. Experiments on standard benchmarks (MVSEC and DSEC datasets) demonstrate unprecedented effectiveness: (i) using purely monocular data, our method achieves comparable results to existing stereo methods; (ii) when applied to stereo data, it strongly outperforms all state-of-the-art (SOTA) approaches, reducing the mean absolute error by at least 42%; (iii) our method also allows for increases in depth completeness by more than 3-fold while still yielding a reduction in median absolute error of at least 30%. Given its remarkable performance and effective processing of event-data, our framework holds strong potential to become a standard approach for using deep learning for event-based depth estimation and SLAM. Project page: https://github.com/tub-rip/DERD-Net

cs.CV↗

arXiv2025

Combined Physics and Event Camera Simulator for Slip Detection

Robot manipulation is a common task in fields like industrial manufacturing. Detecting when objects slip from a robot's grasp is crucial for safe and reliable operation. Event cameras, which register pixel-level brightness changes at high temporal resolution (called ``events''), offer an elegant feature when mounted on a robot's end effector: since they only detect motion relative to their viewpoint, a properly grasped object produces no events, while a slipping object immediately triggers them. To research this feature, representative datasets are essential, both for analytic approaches and for training machine learning models. The majority of current research on slip detection with event-based data is done on real-world scenarios and manual data collection, as well as additional setups for data labeling. This can result in a significant increase in the time required for data collection, a lack of flexibility in scene setups, and a high level of complexity in the repetition of experiments. This paper presents a simulation pipeline for generating slip data using the described camera-gripper configuration in a robot arm, and demonstrates its effectiveness through initial data-driven experiments. The use of a simulator, once it is set up, has the potential to reduce the time spent on data collection, provide the ability to alter the setup at any time, simplify the process of repetition and the generation of arbitrarily large data sets. Two distinct datasets were created and validated through visual inspection and artificial neural networks (ANNs). Visual inspection confirmed photorealistic frame generation and accurate slip modeling, while three ANNs trained on this data achieved high validation accuracy and demonstrated good generalization capabilities on a separate test set, along with initial applicability to real-world data. Project page: https://github.com/tub-rip/event_slip

cs.CV↗

arXiv2025

Gravitational wave memory and geodesic congruence response in generalised Ellis-Bronnikov wormholes

We investigate gravitational wave memory in generalised Ellis-Bronnikov wormhole spacetimes by studying the response of neighbouring timelike geodesics and geodesic congruences to a localized test gravitational wave pulse. Using proper time as the evolution parameter, we identify pulse-induced displacement and velocity memory after subtracting the background geodesic evolution. We further investigate the response of timelike congruences through the expansion and shear using the Raychaudhuri equations. We find that the memory response depends systematically on the wormhole throat radius, the steepness parameter and the location of the pulse relative to the throat. In particular, a pulse at the throat produces zero net integrated expansion but a finite shear response, while a pulse away from the throat generates an asymmetric congruence response. These results show that wormhole geometry can imprint characteristic signatures on both detector-based and congruence-based memory observables.

gr-qc↗

arXiv2024

Gravitational lensing and deflection angles of generalised Ellis-Bronnikov wormhole embedded in a warped braneworld background

We investigate null trajectories, deflection angles, and gravitational lensing in the spacetime of generalized Ellis-Bronnikov (GEB) wormholes and their embedding in a five-dimensional warped braneworld background (WGEB). The GEB geometry extends the standard Ellis-Bronnikov (EB) wormhole by introducing a steepness parameter $m \geq 2$, which controls the shape of the wormhole throat while partially improving on the violation of classical energy conditions. We compare the lensing properties of the four-dimensional GEB geometry with those of its warped five-dimensional counterpart, where the effect of the extra dimension is encoded through the parameter $\delta$, associated with the photon momentum along the extra dimension. Analytic expressions for the deflection angle are obtained in both weak and strong-lensing regimes, and the known EB results are recovered for $m = 2$. For $m > 2$, analytic approximation and numerical analysis is used where exact analytic solutions are not available. We show that the parameter $m$ leaves clear and distinguishable signatures in the deflection angle, Einstein ring radius, and image positions, while the presence of the warped extra dimension modifies the effective impact parameter and leads to a broadening of the photon sphere and lensed images.

gr-qc↗

arXiv2024

Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras

Event cameras are novel bio-inspired vision sensors that measure pixel-wise brightness changes asynchronously instead of images at a given frame rate. They offer promising advantages, namely a high dynamic range, low latency, and minimal motion blur. Modern computer vision algorithms often rely on artificial neural network approaches, which require image-like representations of the data and cannot fully exploit the characteristics of event data. We propose approaches to action recognition based on the Fourier Transform. The approaches are intended to recognize oscillating motion patterns commonly present in nature. In particular, we apply our approaches to a recent dataset of breeding penguins annotated for "ecstatic display", a behavior where the observed penguins flap their wings at a certain frequency. We find that our approaches are both simple and effective, producing slightly lower results than a deep neural network (DNN) while relying just on a tiny fraction of the parameters compared to the DNN (five orders of magnitude fewer parameters). They work well despite the uncontrolled, diverse data present in the dataset. We hope this work opens a new perspective on event-based processing and action recognition.

cs.CV↗

arXiv2024

Event-based Stereo Depth Estimation: A Survey

Stereopsis has widespread appeal in robotics as it is the predominant way by which living beings perceive depth to navigate our 3D world. Event cameras are novel bio-inspired sensors that detect per-pixel brightness changes asynchronously, with very high temporal resolution and high dynamic range, enabling machine perception in high-speed motion and broad illumination conditions. The high temporal precision also benefits stereo matching, making disparity (depth) estimation a popular research area for event cameras ever since its inception. Over the last 30 years, the field has evolved rapidly, from low-latency, low-power circuit design to current deep learning (DL) approaches driven by the computer vision community. The bibliography is vast and difficult to navigate for non-experts due its highly interdisciplinary nature. Past surveys have addressed distinct aspects of this topic, in the context of applications, or focusing only on a specific class of techniques, but have overlooked stereo datasets. This survey provides a comprehensive overview, covering both instantaneous stereo and long-term methods suitable for simultaneous localization and mapping (SLAM), along with theoretical and empirical comparisons. It is the first to extensively review DL methods as well as stereo datasets, even providing practical suggestions for creating new benchmarks to advance the field. The main advantages and challenges faced by event-based stereo depth estimation are also discussed. Despite significant progress, challenges remain in achieving optimal performance in not only accuracy but also efficiency, a cornerstone of event-based computing. We identify several gaps and propose future research directions. We hope this survey inspires future research in this area, by serving as an accessible entry point for newcomers, as well as a practical guide for seasoned researchers in the community.

cs.CV↗

arXiv2024

ES-PTAM: Event-based Stereo Parallel Tracking and Mapping

Visual Odometry (VO) and SLAM are fundamental components for spatial perception in mobile robots. Despite enormous progress in the field, current VO/SLAM systems are limited by their sensors' capability. Event cameras are novel visual sensors that offer advantages to overcome the limitations of standard cameras, enabling robots to expand their operating range to challenging scenarios, such as high-speed motion and high dynamic range illumination. We propose a novel event-based stereo VO system by combining two ideas: a correspondence-free mapping module that estimates depth by maximizing ray density fusion and a tracking module that estimates camera poses by maximizing edge-map alignment. We evaluate the system comprehensively on five real-world datasets, spanning a variety of camera types (manufacturers and spatial resolutions) and scenarios (driving, flying drone, hand-held, egocentric, etc). The quantitative and qualitative results demonstrate that our method outperforms the state of the art in majority of the test sequences by a margin, e.g., trajectory error reduction of 45% on RPG dataset, 61% on DSEC dataset, and 21% on TUM-VIE dataset. To benefit the community and foster research on event-based perception systems, we release the source code and results: https://github.com/tub-rip/ES-PTAM

cs.RO↗

arXiv2024

Signature quasinormal modes of Ellis-Bronnikov wormhole embedded in warped braneworld background

We examine the quasi normal modes of Ellis-Bronnikov wormholes embedded in a warped five dimensional braneworld background and compare with it's four dimensional counterpart. These scalar quasi normal frequencies are obtained using the WKB formula, Prony method and the direct integration method. The signature of the warped extra dimension shows up as two distinct quasi normal ringing era, characterised by two distinct dominant quasi normal modes. Features of the latter region are similar to that observed earlier for massive scalar field in black hole background. We also discuss the how steepness of the neck of the wormhole effects the quasi normal frequencies.

gr-qc↗

arXiv2023

Low-power, Continuous Remote Behavioral Localization with Event Cameras

Researchers in natural science need reliable methods for quantifying animal behavior. Recently, numerous computer vision methods emerged to automate the process. However, observing wild species at remote locations remains a challenging task due to difficult lighting conditions and constraints on power supply and data storage. Event cameras offer unique advantages for battery-dependent remote monitoring due to their low power consumption and high dynamic range capabilities. We use this novel sensor to quantify a behavior in Chinstrap penguins called ecstatic display. We formulate the problem as a temporal action detection task, determining the start and end times of the behavior. For this purpose, we recorded a colony of breeding penguins in Antarctica for several weeks and labeled event data on 16 nests. The developed method consists of a generator of candidate time intervals (proposals) and a classifier of the actions within them. The experiments show that the event cameras' natural response to motion is effective for continuous behavior monitoring and detection, reaching a mean average precision (mAP) of 58% (which increases to 63% in good weather conditions). The results also demonstrate the robustness against various lighting conditions contained in the challenging dataset. The low-power capabilities of the event camera allow it to record significantly longer than with a conventional camera. This work pioneers the use of event cameras for remote wildlife observation, opening new interdisciplinary opportunities. https://tub-rip.github.io/eventpenguins/

cs.CV↗

arXiv2022

Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion

Event cameras are bio-inspired sensors that mimic the human retina by responding to brightness changes in the scene. They generate asynchronous spike-based outputs at microsecond resolution, providing advantages over traditional cameras like high dynamic range, low motion blur and power efficiency. Most event-based stereo methods attempt to exploit the high temporal resolution of the camera and the simultaneity of events across cameras to establish matches and estimate depth. By contrast, this work investigates how to estimate depth from stereo event cameras without explicit data association by fusing back-projected ray densities, and demonstrates its effectiveness on head-mounted camera data, which is recorded in an egocentric fashion. Code and video are available at https://github.com/tub-rip/dvs_mcemvs

cs.CV↗

arXiv2022

Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion

Event cameras are bio-inspired sensors that offer advantages over traditional cameras. They operate asynchronously, sampling the scene at microsecond resolution and producing a stream of brightness changes. This unconventional output has sparked novel computer vision methods to unlock the camera's potential. Here, the problem of event-based stereo 3D reconstruction for SLAM is considered. Most event-based stereo methods attempt to exploit the high temporal resolution of the camera and the simultaneity of events across cameras to establish matches and estimate depth. By contrast, this work investigates how to estimate depth without explicit data association by fusing Disparity Space Images (DSIs) originated in efficient monocular methods. Fusion theory is developed and applied to design multi-camera 3D reconstruction algorithms that produce state-of-the-art results, as confirmed by comparisons with four baseline methods and tests on a variety of available datasets.

cs.CV↗

arXiv2022

Geodesic Congruences in 5D Warped Ellis-Bronnikov Spacetimes

We study the timelike geodesic congruences in the generalized Ellis-Bronnikov spacetime (4D-GEB) and in recently proposed 5D model where a 4D-GEB is embedded in a warped geometry (5D-WGEB) and conduct a comparative study. Analytical expressions of ESR variables (for 4D geometries) are found which reveal the role of the wormhole parameter. In more general 4D and 5D scenarios geodesic equation, geodesic deviation equation and Raychaudhury equations are solved numerically. The evolution of cross-sectional area of the congruences of timelike geodesics (orthogonal to the geodesic flow lines) projected on 2D-surfaces yield an interesting perspective and shows the effects of the wormhole parameter and growing/decaying warp factors. Presence of warping factor triggers rotation or accretion even in the absence of initial congruence rotation. Presence of rotation in the congruence is also found to be playing a crucial role which we discuss in detail.

gr-qc↗

arXiv2022

Geodesics in Generalised Ellis-Bronnikov Spacetime Embedded in Warped 5D Background

We study the particle trajectories in the recently proposed five dimensional warped (generalized) Ellis-Bronnikov spacetime (5D-WGEB) (which does not require exotic matter) as well as it's four dimensional counterpart (4D-GEB) in detail and conduct a comparative study. Analytical approach provides conditions which determines three types of trajectories (trapped, returning and crossing) for both 4D and 5D spacetimes. Notably in 5D geometry existence of trapped trajectories become highly sensitive to the initial conditions. We have solved the timelike geodesic equations numerically and presented the trajectories graphically along with corresponding geodesic potentials. We thus distinguished the roles of the so-called wormhole parameter and the warping factor regarding their effects on the trajectories and embedding as such. Interestingly, the 5D-WGEB model shows the localization of massive particles around the location of the brane for growing warp factor and runaway trajectories for decaying warp factor.

gr-qc↗

arXiv2021

Generalised Ellis-Bronnikov wormholes embedded in warped braneworld background and energy conditions

Ellis-Bronnikov (EB) wormholes require violation of null energy conditions at the `throat'. This problem was cured by a simple modification of the `shape function', which introduces a new parameter $m\ge 2$ ($m=2$ corresponds to the EB model). This leads to a generalised (GEB) version. In this work, we consider a model where the GEB wormhole geometry is embedded in a five dimensional warped background. We studied the status of all the energy conditions in detail for both EB and GEB embedding. We present our results analytically (wherever possible) and graphically. Remarkably, the presence of decaying warp factor leads to satisfaction of weak energy conditions even for the EB geometry, while the status of all the other energy conditions are improved compared to the four dimensional scenario. Besides inventing a new way to avoid the presence of exotic matter, in order to form a wormhole passage, our work reveals yet another advantage of having a warped extra dimension.

gr-qc↗

arXiv2021

Blind Updates in Coded Caching

We consider the centralized coded caching system where a library of files is available at the server and their subfiles are cached at the clients as prescribed by a placement delivery array (PDA). We are interested in the problem where a specific file in the library is replaced with a new file at the server, the contents of which are correlated with the file being replaced, and this change needs to be communicated to the caches. Upon replacement, the server has access only to the updated file and is unaware of its differences with the original, while each cache has access to specific subfiles of the original file as dictated by the PDA. We model the correlation between the two files by assuming that they differ in at the most $ε$ subfiles, and aim to reduce the number of bits broadcast by the server to update the caches. We design a new elegant coded transmission strategy for the server to update the caches blindly, and also identify a simple scheme that is based on MDS codes. We then derive converse bounds on the minimum communication cost $\ell^*$ among all linear strategies. For two well-known families of PDAs -- Maddah-Ali & Niesen's caching scheme and a PDA by Tang & Ramamoorthy and Yan et al. -- our new scheme has cost $\ell^*(1 + o(1))$ when the updates are sufficiently sparse, while the scheme using MDS codes has order-optimal cost when the updates are dense.

cs.IT↗

arXiv2021

Versailles-FP dataset: Wall Detection in Ancient

Access to historical monuments' floor plans over a time period is necessary to understand the architectural evolution and history. Such knowledge bases also helps to rebuild the history by establishing connection between different event, person and facts which are once part of the buildings. Since the two-dimensional plans do not capture the entire space, 3D modeling sheds new light on the reading of these unique archives and thus opens up great perspectives for understanding the ancient states of the monument. Since the first step in the building's or monument's 3D model is the wall detection in the floor plan, we introduce in this paper the new and unique Versailles FP dataset of wall groundtruthed images of the Versailles Palace dated between 17th and 18th century. The dataset's wall masks are generated using an automatic approach based on multi directional steerable filters. The generated wall masks are then validated and corrected manually. We validate our approach of wall mask generation in state-of-the-art modern datasets. Finally we propose a U net based convolutional framework for wall detection. Our method achieves state of the art result surpassing fully connected network based approach.

cs.CV↗