Title: Learned Two-Plane Perspective Prior based Image Resampling for Efficient Object Detection Abstract:    Real-time efficient perception is critical for autonomous navigation and city scale sensing. Orthogonal to architectural improvements, streaming perception approaches have exploited adaptive sampling improving real-time detection performance. In this work, we propose a learnable geometry-guided prior that incorporates rough geometry of the [...]

Title: Using Drones and Remote Sensing to Understand Forests with Limited Labeled Data Abstract: Drones and remote sensing can provide observations of forests at scale, but this raw data needs to be interpreted to further scientific understanding and inform effective management decisions. This thesis studies two problems under the realistic constraint of limited domain-specific training [...]

Title: Lights, Camera, Render: Neural Fields for Structured Lighting Abstract: 3D scene reconstruction from 2D image supervision alone is an under-constrained problem. Recent neural rendering frameworks have made great strides in learning 3D scene representations to enable novel view synthesis, but they struggle to reconstruct geometry of low-texture regions or from sparse views. The prevalence of active [...]

Title: Learning to See in the Dark and Beyond Abstract: Robotic Perception in diverse domains such as low-light scenarios remains a challenge, even upon the employment of new sensing modalities like thermal imaging and specialized night-vision sensors. This is largely due to the high difficulty in obtaining labeled data for certain tasks. In this work, [...]

Title: Self-Supervised Costmap Learning for Off-Road Vehicle Traversability Abstract: Estimating terrain traversability in off-road environments requires reasoning about complex interaction dynamics between the robot and these terrains. However, it is challenging to build an accurate physics model, or create informative labels to learn a model in a supervised manner, for these interactions. We propose a method [...]

Title: On Generalization and Benchmarking on Physical Robots   Abstract: Robotics research has seen significant advancements; however, the field remains predominantly demo-driven, making direct comparisons between methods difficult without replicating them on individual setups. While many simulation benchmarks exist, they usually feature contrived datasets and do not accurately reflect real-world performance. In my thesis, we [...]