# Indoor Obstacle Discovery on Reflective Ground via Monocular Camera

arXiv: https://arxiv.org/abs/2401.01445 · HTML: https://mlsanity.com/paper/2401.01445/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 57 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1606.02147/) (2016)
- [Detecting the Unexpected via Image Resynthesis](https://mlsanity.com/paper/1904.07595/) (2019)
- [Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles](https://mlsanity.com/paper/1609.04653/) (2016)
- [Unsupervised Scale-consistent Depth Learning from Video](https://mlsanity.com/paper/2105.11610/) (2021)
- [Location-aware Single Image Reflection Removal](https://mlsanity.com/paper/2012.07131/) (2020)
- [Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving Images](https://mlsanity.com/paper/2002.10570/) (2020)
- [Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling](https://mlsanity.com/paper/1612.06573/) (2016)
- [Neural Image Representations for Multi-Image Fusion and Layer Separation](https://mlsanity.com/paper/2108.01199/) (2021)
- [LiDAR guided Small obstacle Segmentation](https://mlsanity.com/paper/2003.05970/) (2020)
- [Occlusion-shared and Feature-separated Network for Occlusion Relationship Reasoning](https://mlsanity.com/paper/1908.05898/) (2019)
- [A Novel Multi-layer Framework for Tiny Obstacle Discovery](https://mlsanity.com/paper/1904.10161/) (2019)
- [Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection](https://mlsanity.com/paper/1709.04347/) (2017)
- [Small Obstacle Avoidance Based on RGB-D Semantic Segmentation](https://mlsanity.com/paper/1908.11675/) (2019)
- [MergeNet: A Deep Net Architecture for Small Obstacle Discovery](https://mlsanity.com/paper/1803.06508/) (2018)
- [J-MOD2: Joint Monocular Obstacle Detection and Depth Estimation](https://mlsanity.com/paper/1709.08480/) (2017)
- [Tiny Obstacle Discovery by Occlusion-Aware Multilayer Regression](https://mlsanity.com/paper/2111.09204/) (2021)
- [Boundary-induced and scene-aggregated network for monocular depth prediction](https://mlsanity.com/paper/2102.13258/) (2021)
- [DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers](https://mlsanity.com/paper/1606.04702/) (2016)

## Built on by (most cited first, 8 of 8)

- [AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios](https://mlsanity.com/paper/2410.14379/) (2024)
- [Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation](https://mlsanity.com/paper/2406.06813/) (2024)
- [Exploiting Low-level Representations for Ultra-Fast Road Segmentation](https://mlsanity.com/paper/2402.02430/) (2024)
- [LDTrack: Dynamic People Tracking by Service Robots using Diffusion Models](https://mlsanity.com/paper/2402.08774/) (2024)
- [SM4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Model](https://mlsanity.com/paper/2403.08556/) (2024)
- [Cues3D: Unleashing the Power of Sole NeRF for Consistent and Unique Instances in Open-Vocabulary 3D Panoptic Segmentation](https://mlsanity.com/paper/2505.00378/) (2025)
- [PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents](https://mlsanity.com/paper/2605.10341/) (2026)
- [The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery](https://mlsanity.com/paper/2604.14176/) (2026)

## Abstract (arXiv)

Visual obstacle discovery is a key step towards autonomous navigation of indoor mobile robots. Successful solutions have many applications in multiple scenes. One of the exceptions is the reflective ground. In this case, the reflections on the floor resemble the true world, which confuses the obstacle discovery and leaves navigation unsuccessful. We argue that the key to this problem lies in obtaining discriminative features for reflections and obstacles. Note that obstacle and reflection can be separated by the ground plane in 3D space. With this observation, we firstly introduce a pre-calibration based ground detection scheme that uses robot motion to predict the ground plane. Due to the immunity of robot motion to reflection, this scheme avoids failed ground detection caused by reflection. Given the detected ground, we design a ground-pixel parallax to describe the location of a pixel relative to the ground. Based on this, a unified appearance-geometry feature representation is proposed to describe objects inside rectangular boxes. Eventually, based on segmenting by detection framework, an appearance-geometry fusion regressor is designed to utilize the proposed feature to discover the obstacles. It also prevents our model from concentrating too much on parts of obstacles instead of whole obstacles. For evaluation, we introduce a new dataset for Obstacle on Reflective Ground (ORG), which comprises 15 scenes with various ground reflections, a total of more than 200 image sequences and 3400 RGB images. The pixel-wise annotations of ground and obstacle provide a comparison to our method and other methods. By reducing the misdetection of the reflection, the proposed approach outperforms others. The source code and the dataset will be available at https://github.com/XuefengBUPT/IndoorObstacleDiscovery-RG.