# CADENCE: Context-Adaptive Depth Estimation for Navigation and Computational Efficiency

arXiv: https://arxiv.org/abs/2604.07286 · HTML: https://mlsanity.com/paper/2604.07286/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Slimmable Neural Networks](https://mlsanity.com/paper/1812.08928/) (2018)
- [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021)
- [SqueezeNext: Hardware-Aware Neural Network Design](https://mlsanity.com/paper/1803.10615/) (2018)
- [Representation Learning for Event-based Visuomotor Policies](https://mlsanity.com/paper/2103.00806/) (2021)
- [HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception](https://mlsanity.com/paper/2201.06644/) (2022)
- [QONNX: Representing Arbitrary-Precision Quantized Neural Networks](https://mlsanity.com/paper/2206.07527/) (2022)
- [Dynamic Slimmable Denoising Network](https://mlsanity.com/paper/2110.08940/) (2021)
- [Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems](https://mlsanity.com/paper/2306.12691/) (2023)
- [NaviSplit: Dynamic Multi-Branch Split DNNs for Efficient Distributed Autonomous Navigation](https://mlsanity.com/paper/2406.13086/) (2024)
- [NaviSlim: Adaptive Context-Aware Navigation and Sensing via Dynamic Slimmable Networks](https://mlsanity.com/paper/2407.01563/) (2024)

## Abstract (arXiv)

Autonomous vehicles deployed in remote environments typically rely on embedded processors, compact batteries, and lightweight sensors. These hardware limitations conflict with the need to derive robust representations of the environment, which often requires executing computationally intensive deep neural networks for perception. To address this challenge, we present CADENCE, an adaptive system that dynamically scales the computational complexity of a slimmable monocular depth estimation network in response to navigation needs and environmental context. By closing the loop between perception fidelity and actuation requirements, CADENCE ensures high-precision computing is only used when mission-critical. We conduct evaluations on our released open-source testbed that integrates Microsoft AirSim with an NVIDIA Jetson Orin Nano. As compared to a state-of-the-art static approach, CADENCE decreases sensor acquisitions, power consumption, and inference latency by 9.67%, 16.1%, and 74.8%, respectively. The results demonstrate an overall reduction in energy expenditure by 75.0%, along with an increase in navigation accuracy by 7.43%.