Pranay Meshram

I’m a Ph.D. student at the University at Buffalo working with Dr. Karthik Dantu at DRONES Lab.

In August 2026, our paper CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments was accepted to IEEE Robotics and Automation Letters (RA-L). Project page: CLEAR.

My research focuses on building robust perception and planning systems for autonomous robots operating in unstructured environments. I work on visual SLAM, 3D reconstruction quality metrics, semantic-geometric terrain abstraction, and self-supervised depth estimation from polarization. My work spans from developing efficient edge-based perception models to large-scale planning frameworks that enable reliable autonomy in challenging real-world conditions.

I’ve led teams to top results in hardware-efficient autonomy—1st in latency at the 2022 ACM/IEEE TinyML Contest (overall 5th) and 4th place at DAC SDC 2022. Previously, I was a Research Scientist Intern at Meta Reality Labs working on self-supervised stereo depth estimation.

Resume

news

Aug 06, 2026	Our paper “CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments” has been accepted to IEEE Robotics and Automation Letters (RA-L). For project details, visit the CLEAR project page.
Dec 09, 2025	Our paper “QAL: A Loss for Recall–Precision Balance in 3D Reconstruction” has been accepted to WACV 2026! 🎉
Nov 02, 2025	UBPercept placed 5th overall at the 2022 ACM/IEEE TinyML Design Contest at ICCAD (Nov. 2), and earned 1st in latency and 3rd in flash (memory). Full results: TinyML Contest Winners.
Jul 08, 2022	Built a monocular visual odometry (VO) pipeline in Rust on the KITTI benchmark. Implemented feature tracking, pose estimation, and trajectory reconstruction, with plotting utilities to compare estimated trajectories against ground truth. Benchmarked VO accuracy across sequences and experimented with tuning feature detection thresholds and RANSAC parameters to handle motion blur and texture-poor regions. Added demos and visualizations to highlight drift over long trajectories and loop-closure opportunities. Git Repository
Jun 21, 2022	DAC System Design Contest 2022 – 4th place (UBPercept). Built an FPGA-friendly CNN pipeline on Ultra96V2 with quantization-aware training and deployment automation, targeting tight latency/memory budgets. Led a 7-member team through data curation, profiling, and inference optimization; automated builds and on-board evaluation to iterate rapidly on accuracy–efficiency trade-offs. Documented lessons on model pruning and kernel fusion for edge devices. Results

selected publications

RA-L
CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments

Pranay Meshram, Charuvahan Adhivarahan, Ehsan Tarkesh Esfahani, and 3 more authors

IEEE Robotics and Automation Letters, 2026

Abs arXiv Bib HTML

Long-horizon navigation in unstructured environments demands terrain abstractions that scale to tens of km\textsuperscript2 while preserving semantic and geometric structure, which neither uniform grids nor quadtrees provide. CLEAR combines boundary-aware spatial decomposition with recursive plane fitting to produce convex, semantically aligned regions encoded as a terrain-aware graph, enabling faster and more reliable planning on large areas.
@article{meshram2026clear, title = {CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments}, author = {Meshram, Pranay and Adhivarahan, Charuvahan and Esfahani, Ehsan Tarkesh and Chowdhury, Souma and Wang, Chen and Dantu, Karthik}, journal = {IEEE Robotics and Automation Letters}, year = {2026}, webpage = {https://droneslab.github.io/CLEAR/} }
WACV
QAL: A Loss for Recall–Precision Balance in 3D Reconstruction

Pranay Meshram, Yash Turkar, Kartikeya Singh, and 3 more authors

In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Mar 2026

Abs DOI arXiv Bib HTML

Volumetric learning underpins many 3D vision tasks such as completion, reconstruction, and mesh generation, yet training objectives still rely on Chamfer Distance (CD) or Earth Mover’s Distance (EMD), which fail to balance recall and precision. We propose Quality-Aware Loss (QAL), a drop-in replacement for CD/EMD that combines a coverage-weighted nearest-neighbor term with an uncovered–ground-truth attraction term, explicitly decoupling recall and precision into tunable components.
@inproceedings{meshram2026qal, title = {QAL: A Loss for Recall--Precision Balance in 3D Reconstruction}, author = {Meshram, Pranay and Turkar, Yash and Singh, Kartikeya and Masilamani, Praveen Raj and Adhivarahan, Charuvahan and Dantu, Karthik}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, pages = {7965--7974}, month = mar, year = {2026}, doi = {10.1109/WACV61042.2026.00769} }
CRV
Empir3D: A Framework for Multi-Dimensional Point Cloud Assessment

Yash Turkar, Pranay Meshram, Christo Aluckal, and 2 more authors

In Proceedings of the 23rd Conference on Robots and Vision (CRV), May 2026

Abs DOI arXiv Bib HTML

Empir3D proposes a multi-dimensional point cloud evaluation framework with metrics for resolution, accuracy, coverage, and artifacts, providing richer assessment of SLAM and point cloud completion results beyond traditional Chamfer or EMD.
@inproceedings{turkar2026empir3d, title = {Empir3D: A Framework for Multi-Dimensional Point Cloud Assessment}, author = {Turkar, Yash and Meshram, Pranay and Aluckal, Christo and Adhivarahan, Charuvahan and Dantu, Karthik}, booktitle = {Proceedings of the 23rd Conference on Robots and Vision (CRV)}, month = may, year = {2026}, doi = {10.21428/d82e957c.900da59a}, }