SIH26053/Smart Vehicles

Variable resolution Lidar elevation map

A software framework that transforms Lidar point clouds into a variable resolution 2.5D elevation map for autonomous vehicle perception.

DRDO · Department of Defence Production /IDEX · Software

Teams already in: Not read yet
Buildable in 36 hours: LowOur estimate, from their wording
Hard blockers: NoneNothing you must source first
Submissions close: 15 Sept9 days left

Make this one of our two Compare with others

Signing in saves it for your whole team — everyone on your invite link sees the same two entries. Anything you shortlisted while signed out comes with you.

Submit on sih.gov.in

Whether this suits your team

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
Deep Learning Pipeline
A neural network that performs semantic segmentation on point clouds to classify terrain, static obstacles, and moving objects.
02
Variable Resolution Engine
An algorithm that projects 3D points into a non-uniform 2.5D grid with high detail nearby and lower detail at a distance.
03
Real-time Visualization
A dashboard displaying the 2.5D map with color-coded terrain and objects while showing memory usage reduction.

What they ask for, and what they leave open

Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.

They state

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.

They never say

specific benchmark datasets to use for training and testing
target hardware specifications for measuring low latency and frames per second

A jury can still ask about these. Decide them deliberately rather than by accident.

What they'll ask you

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 more— the 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.

Before you commit

Each one is quoted from a gap in the brief, not a guess about your team.

No measurable target
The text asks for evidence of low latency, high FPS, and high accuracy without defining specific numerical thresholds for success.
No specific user named
The description mentions autonomous navigation and vehicles in general but never names the specific end user operating the software.

The data

What the organisers attached, and what the brief assumes you can get.

Dataset from the organisers: None attached
Does the brief need data?: Nothing in the text says it depends on a dataset we would have to find.

Also from DRDO

Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26053, set by DRDO.
Its official title, word for word: "Adaptive Variable Resolution 2.5D Lidar Mapping for Dynamic Environment Perception"
Theme: Smart Vehicles. Category: software.

In plain terms: A software framework that transforms Lidar point clouds into a variable resolution 2.5D elevation map for autonomous vehicle perception.

## The organisers' full description, verbatim

• 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.

## What the brief never says

- specific benchmark datasets to use for training and testing
- target hardware specifications for measuring low latency and frames per second
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- Known obstacles in the brief: No target; No named user.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

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Problem statement facts assembled by Wynit from DRDO's published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

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.