SIH26053: Adaptive Variable Resolution 2.5D Lidar Mapping for Dynamic Environment Percepti — SIH 2026 | Wynit

SIH26053: Adaptive Variable Resolution 2.5D Lidar Mapping for Dynamic Environment Percepti — SIH 2026 | Wynit

What they'll ask you

10

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Specific benchmark datasets to use for training and testing?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Target hardware specifications for measuring low latency and frames per second?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.

7 morethe ones a panel gets to if there is time

The brief asks for deep learning pipeline. How would you build that?
Decoded from SIH26053 itself: A neural network that performs semantic segmentation on point clouds to classify terrain, static obstacles, and moving objects. The brief asks for it by name.
The brief asks for variable resolution engine. How would you build that?
Decoded from SIH26053 itself: An algorithm that projects 3D points into a non-uniform 2.5D grid with high detail nearby and lower detail at a distance. The brief asks for it by name.
The brief asks for real-time visualization. How would you build that?
Decoded from SIH26053 itself: A dashboard displaying the 2.5D map with color-coded terrain and objects while showing memory usage reduction. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the jury will check for low latency, high frames per second, and high classification accuracy across varying distances, as well as a demonstrated reduction in memory usage.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (325 words)

• Background: Autonomous navigation depends on the ability of a vehicle to perceive its surroundings with high precision. While 3D Lidar point clouds provide rich spatial data, processing millions of points in real-time creates immense computational bottlenecks and memory latency. Conversely, standard 2D occupancy grids lose critical height information necessary for detecting curbs, potholes, or overhanging obstacles. To balance precision and performance, there is a need for a 'foveated' mapping approachâ€”similar to human visionâ€” where the immediate vicinity is rendered in high detail for safety, and distant areas are simplified to reduce the processing load. • Description: The goal is to build a deep learning pipeline that transforms raw Lidar point clouds into a variable resolution 2.5D grid (an elevation map with semantic layers). The system must perform three primary tasks: 1. Terrain Analysis: Distinguish between drivable surfaces and non-drivable terrain. 2. Object Detection: Identify and classify static obstacles (walls, poles) and dynamic objects (pedestrians, other vehicles). 3. Adaptive Spatial Representation: Implement a non-uniform grid where the cell size increases as the distance from the sensor increases. This requires a sophisticated data structure that can handle variable resolution without causing alignment errors or data loss during the projection from 3D to 2.5D. • Expected Solution: A software framework consisting of: • A Deep Learning Model: A network (e.g., PointNet++ or a Sparse Convolutional Neural Network) capable of semantic segmentation of point clouds into terrain, static obstacles, and moving objects. • Variable Resolution Grid Engine: An algorithm that projects classified 3D points into a 2.5D grid where the resolution is high (e.g., 5cm cells) within a 10m radius and decreases (e.g., 50cm cells) up to a 100m radius. • Real-time Visualization: A dashboard showing the 2.5D map with distinct color-coding for terrain and objects, demonstrating a significant reduction in memory usage compared to a uniform high-resolution 3D map. • Performance Metrics: Evidence of low latency (high FPS) and high accuracy in object classification across varying distances.