Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data

Accurate estimation of forest biomass is crucial for monitoring carbon sequestration and informing climate change mitigation strategies. Existing methods often rely on allometric models, which estimate individual tree biomass by relating it to measurable biophysical parameters, e.g., trunk diameter and height. This indirect approach is limited in accuracy due to measurement uncertainties and the inherently approximate nature of allometric equations, which may not fully account for the variability in tree characteristics and forest conditions. This study proposes a direct approach that leverages synthetic point cloud data to train a deep regression network, which is then applied to real point clouds for plot-level wood volume and aboveground biomass (AGB) estimation. We created synthetic 3D forest plots with ground truth volume, which were then converted into point cloud data using a lidar simulator. These point clouds were subsequently used to train deep regression networks based on PointNet, PointNet++, DGCNN, and PointConv. When applied to synthetic data, the deep regression networks achieved mean absolute percentage error (MAPE) values ranging from 1.69% to 8.11%. The trained networks were then applied to real lidar data to estimate volume and AGB. When compared against field measurements, our direct approach showed discrepancies of 2% to 20%. In contrast, indirect approaches based on individual tree segmentation followed by allometric conversion, as well as FullCAM, exhibited substantially large underestimation, with discrepancies ranging from 27% to 85%. Our results highlight the potential of integrating synthetic data with deep learning for efficient and scalable forest carbon estimation at plot level.

Relative Energy Learning for LiDAR Out-of-Distribution Detection

Out-of-distribution (OOD) detection is a critical requirement for reliable autonomous driving, where safety depends on recognizing road obstacles and unexpected objects beyond the training distribution. Despite extensive research on OOD detection in 2D images, direct transfer to 3D LiDAR point clouds has been proven ineffective. Current LiDAR OOD methods struggle to distinguish rare anomalies from common classes, leading to high false-positive rates and overconfident errors in safety-critical settings. We propose Relative Energy Learning (REL), a simple yet effective framework for OOD detection in LiDAR point clouds. REL leverages the energy gap between positive (in-distribution) and negative logits as a relative scoring function, mitigating calibration issues in raw energy values and improving robustness across various scenes. To address the absence of OOD samples during training, we propose a lightweight data synthesis strategy called Point Raise, which perturbs existing point clouds to generate auxiliary anomalies without altering the inlier semantics. Evaluated on SemanticKITTI and the Spotting the Unexpected (STU) benchmark, REL consistently outperforms existing methods by a large margin. Our results highlight that modeling relative energy, combined with simple synthetic outliers, provides a principled and scalable solution for reliable OOD detection in open-world autonomous driving.

From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects

Traditional object detection methods operate under the closed-set assumption, where models can only detect a fixed number of objects predefined in the training set. Recent works on open vocabulary object detection (OVD) enable the detection of objects defined by an unbounded vocabulary, which reduces the cost of training models for specific tasks. However, OVD heavily relies on accurate prompts provided by an ''oracle'', which limits their use in critical applications such as driving scene perception. OVD models tend to misclassify near-out-of-distribution (NOOD) objects that have similar semantics to known classes, and ignore far-out-of-distribution (FOOD) objects. To address theses limitations, we propose a framework that enables OVD models to operate in open world settings, by identifying and incrementally learning novel objects. To detect FOOD objects, we propose Open World Embedding Learning (OWEL) and introduce the concept of Pseudo Unknown Embedding which infers the location of unknown classes in a continuous semantic space based on the information of known classes. We also propose Multi-Scale Contrastive Anchor Learning (MSCAL), which enables the identification of misclassified unknown objects by promoting the intra-class consistency of object embeddings at different scales. The proposed method achieves state-of-the-art performance in common open world object detection and autonomous driving benchmarks.