PhysicalAI Autonomous Vehicles NuRec-AV-Object-Benchmark

Dataset Description

The NuRec-AV-Object-Benchmark is an object-centric benchmark for evaluating image-to-3D reconstruction systems on autonomous vehicle data. Introduced alongside Asset Harvester, it is designed to support systematic evaluation of in-the-wild AV object reconstruction under realistic viewpoint bias and sensor noise.

Unlike curated object datasets with dense coverage, this benchmark reflects the sparse and imperfect observation regime found in real driving logs. Objects are often seen from only one or a few views, with heavy occlusion, motion blur, noisy calibration, rolling-shutter effects, and imperfect geometric alignment.

Each sample is organized under a semantic object category and a sample identifier, and includes object-centric RGB crops, foreground masks, and camera metadata. The benchmark is distributed in two complementary parts:

• Part_A: a held-out-view evaluation split with input_views/ and reserved_views/
• Part_B: a harder no-ground-truth split with input_views/ only

Supported categories

• commercial_vehicles
• consumer_vehicles
• other_objects
• VRU_pedestrians
• VRU_riders

Dataset Structure

Part_A: held-out-view evaluation

Part_A provides input_views/ together with reserved_views/ that are not used as model input. These reserved views act as held-out reference targets for quantitative evaluation.

Each Part_A sample contains:

• input_views/
• reserved_views/
• per-view frame_XX.jpeg
• per-view mask_XX.png
• camera.json

Part_B: hard no-ground-truth split

Part_B is intentionally more challenging. It contains stronger motion blur, heavier occlusion, and narrower view coverage. No reserved reference views are provided, so this split is intended for harder qualitative or perceptual evaluation settings.

Each Part_B sample contains:

• input_views/
• per-view frame_XX.jpeg
• per-view mask_XX.png
• camera.json

Dataset summary

• Total samples: 3716
• Part_A: 2206 samples
• Part_B: 1510 samples

Split composition

Part_A

• commercial_vehicles: 308
• consumer_vehicles: 1472
• other_objects: 55
• VRU_pedestrians: 330
• VRU_riders: 41

Part_B

• commercial_vehicles: 405
• consumer_vehicles: 602
• other_objects: 90
• VRU_pedestrians: 383
• VRU_riders: 30

Creation date

• Dataset creation date: 2026-03-25

Reference

• Asset Harvester